# Learning from synthesized data for quality assurance in open-source microcontroller manufacturing

Zhifan Song, Abd Al Rahman M. Abu Ebayyeh

► To cite this version:

Zhifan Song, Abd Al Rahman M. Abu Ebayyeh. Learning from synthesized data for quality assurance in open-source microcontroller manufacturing. Measurement - Journal of the International Measurement Confederation (IMEKO), 2025, 253, pp.117490. 10.1016/j.measurement.2025.117490. hal-05456849

HAL Id: hal-05456849

https://hal.sorbonne-universite.fr/hal-05456849v1

Submitted on 13 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

# Learning from synthesized data for quality assurance in open-source microcontroller manufacturing

Zhifan Song<sup>a,b</sup>, Abd Al Rahman M. Abu Ebayyeh<sup>b,c</sup>,*

## ARTICLE INFO

### Keywords:

- Deep learning

- Quality assurance

- Defect detection

- YOLO

## ABSTRACT

The proliferation of Arduino has led to numerous low-cost replicas, complicating defect detection due to style variability. Existing detectors struggle to generalize with synthetic data. To address this, we introduce Context-Guided Triplet Attention YOLO-Faster (CGTA-YOLO-F), a real-time model that enhances feature extraction through CGTA blocks, along with a novel C2f-FCGA block (Faster Context Guidance with simplified Attention) for enhancing multi-scale feature fusion. Trained on synthesized data and tested on real data, the method achieves 97.4% mean average precision (mAP) for component detection, outperforming YOLOv8 and YOLOv10 by 3% and 3.4%. It also achieves 91.4% accuracy for misalignment classification, 7.1% higher than the baseline. The model performs well on two additional datasets and integrates detection and classification into a unified framework. It is efficient in speed and memory, making it practical for industrial defect detection tasks.

## 1. Introduction

Microcontrollers play an important role in electronic devices and embedded systems, requiring precise component placement on the Printed Circuit Board (PCB) to achieve optimal performance [1]. Open-source microcontrollers, such as the Arduino Uno, are more affordable and accessible. Arduino's open-source nature allows third-party manufacturers to produce and sell their own versions of the board, often at substantially lower costs. This practice is more prevalent among smaller and financially constrained factories that rely on manual inspection processes which present challenges in maintaining consistent quality and productivity [2].

Quality assurance in PCB manufacturing includes several procedures, such as layout design, component placement, routing, and power distribution [3]. Beyond these fundamental steps, PCBs are also susceptible to backside circuitry defects [4]. Front-side defects are also common, such as solder joint problems, missing components, and alignment issues like shifts and rotations [5]. These defects highlight the necessity of good quality control measures to guarantee the integrity and reliability of Arduino boards. Traditional Automatic Optical Inspection (AOI) systems, while significantly enhancing accuracy and efficiency in defect detection, often require secondary human inspection to ensure reliability [6]. These conventional systems are finding it difficult to keep up with the demands of modern production standards

as modern industrial manufacturing develops due to the increasing integration of electronics into daily life, the quick rollout of 5G technology, and the growth of Internet of Things (IoT) devices.

Deep learning has emerged in object detection, as demonstrated by one-stage real-time models like You Only Look Once (YOLO) [7], which offer faster inference and higher accuracy than two-stage models like Faster Region-based Convolutional Neural Network (R-CNN) [8]. Transformer-based models [9] have also shown remarkable performance, especially the Detection Transformer (DETR) [10]. However, they often have significantly more parameters, leading to slower speeds on lower-end hardware. The combination of precision and real-time capabilities has advanced thanks to Real-Time Detection Transformer (RT-DETR) [11]. However, the base model is still a lot more computationally expensive than the nano or tiny versions of YOLO, which makes them less appropriate for settings with limited resources, such as distributed setup with edge computing, or smaller factories. As a result, convolutional-based models, notably the YOLO family, remain the best foundation for lightweight and effective real-time object identification in various of industrial applications [12–14]. The efficacy of deep learning-based AOI systems in contemporary manufacturing has been demonstrated by their growing adoption in surface mount technology (SMT) assembly [15], and in surface defect detection [16–18].

The YOLO family has demonstrated notable progress in industrial manufacturing and specifically defect detection applications [19], with

<footnote><sup>a</sup> LIP6 Laboratory, Sorbonne University, CNRS UMR 7606, Paris 75252, France</footnote>

<footnote><sup>b</sup> Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: zhifan song@lip6.fr (Z. Song), a.abu-ebayyeh@imperial.ac.uk (A.M. Abu Ebayyeh).</footnote>
<figure><img src="image_4.png" /><figcaption>Example illustration of component style variation</figcaption></figure>

<caption>**Fig. 1.** Various Arduino Uno boards highlighting design and quality differences. The top left shows the official board, while the red box groups third-party boards. The blue bounding box highlights zoomed-in views of some constituent components as example, providing better visual clarity of the differences. The open-source nature of Arduino enables different manufacturers to produce and sell their own versions, resulting in distinct styles and component variations.</caption>

each iteration enhancing detection accuracy and speed to meet indus-
trial demands. The latest YOLO versions, YOLOv8 [20], YOLOv9 [21],
and YOLOv10 [22] introduce standout improvements. YOLOv9 lever-
ages programmable gradient information and generalized efficient
layer aggregation to mitigate information loss, resulting in higher
detection accuracy. However, YOLOv8 exhibits faster inference speeds,
making it more suitable for real-time applications [23], a trend also ob-
served in our specific application, which will be discussed in Section 4.
YOLOv10 advances further by eliminating non-maximum suppression
(NMS) and incorporating a novel partial self-attention (PSA) into
its backbone which enhances feature extraction and inference speed
simultaneously.

Although the detection of PCB defects has been investigated in a
number of studies [4,12,24], Most of them only focused on backside
circuitry defects, and either prioritized accuracy or detection speed
over addressing both or struck a balance. For electronic component
detection, most studies focused on a limited range of components [25–
27] or relied on expensive equipment [28] or computationally ex-
pensive algorithms [29] with limited detection capabilities. To the
best of our knowledge, no relevant work has specifically focused on
Arduino boards or addressing component, missing and misalignment
component detection within a unified model. Moreover, due to the
open-source nature of Arduino, a diverse array of replicas has emerged,
each with unique design features and component variations, arising
from the varying manufacturing practices and supplier agreements.
Fig. 1 illustrates the differences with some sample images: the top
left image represents the official Arduino board and the remaining
images display multiple replicas. Despite similarities in basic design,
the figure shows the impact of different designs and component sourc-
ing on the final product. Therefore, constructing a comprehensive
dataset including defects from various board styles poses a significant
challenge. Furthermore, manually creating defects on actual products
to generate a comprehensive dataset is not only labor-intensive [30],
but is also financially burdensome and poses potential environmental
concerns [31]. One possible solution is to generate a synthetic training
dataset, a common approach in related research [26,32]. However, they

lack investigations into the generalization gap between synthetic and
real data, and despite the advances of YOLO and DETR, these existing
state-of-the-art (SOTA) general-purpose object detection models still
face challenges in generalizing from synthetic to real-world data, a
critical requirement for our application.

We aim to create a system tailored for Arduino Uno boards to fill
this gap, and it successfully generalizes to real-world Arduino images
of unseen styles during the training phase. Our model outperforms
current real-time object detectors in both tasks, concentrating on front-
side electronic component quality assurance with the dedicated dataset
and backside defect identification based on two publicly available and
widely used PCB defect datasets.

Our proposed method, CGTA-YOLO-F, addresses these limitations
with several key innovations made for real-time quality assurance
and defect detection in resource-constrained environments. First, we
replace conventional convolutions with context-guided blocks (CGB)
[33], enabling more effective context-aware feature extraction while
maintaining computational efficiency. Additionally, Triplet Attention
(TA) [34] is integrated into the backbone, providing a lightweight yet
better-performing solution compared to the coupling of CGB with al-
ternative attention mechanisms in generalization from synthetic train-
ing, as demonstrated in Section 4.4.3. We further introduce a novel
bottleneck design in the C2f architecture, improving multi-scale fea-
ture fusion and boosting detection performance, ensuring real-time
performance on both high-end and resource-constrained hardware.
These innovations make CGTA-YOLO-F a versatile and efficient solution
for real-time quality assurance, setting a new benchmark in micro-
controller defect detection by effectively generalizing from synthetic
training data to real-world applications.

Our key contributions are as follows:

* Proposed a unified framework for defect detection and classification in microcontroller circuits, addressing components, misalignments, and circuitry defects.

* Introduced context-guided blocks with triplet attention for improved backbone feature extraction and a novel Faster Context
Guidance with simplified Attention (FCGA) C2f bottleneck for enhanced multi-scale feature fusion.

* Applied hardware acceleration techniques to the proposed model, showcasing real-time adaptability to various hardware setups, and validated the framework on multiple datasets, demonstrating superior performance and speed for real-time applications.

The structure of the paper is as follows: Section 2 reviews existing studies on electronic component detection, PCB defect detection, and training with synthesized data. Section 3 describes the three datasets used and details the technical aspects of proposed method, highlighting its improvements over the baseline. Section 4 presents experimental results and ablation studies across the three datasets, comparing the proposed model with existing methods to demonstrate its effectiveness. This section also includes a discussion on hardware acceleration techniques and their necessity to real-time performance. Finally, Section 5 concludes the paper.

## 2. Related work

In this section, we review studies on electronic component identification, PCB defect detection, and training from synthetic data for generalization in real-word applications, and discuss research gaps.

### 2.1. Electronic component detection

In electronic component detection, early techniques mostly depend on template matching techniques, using methods such as normalized cross-correlation (NCC) [29,35] for detecting misplacements in PCBs. However, these methods were primarily focused on certain components, such as resistors, and were limited by slow detection speeds. More recent efforts have leveraged deep learning, including a YOLOv3-based component detector [36] for capacitors. Similarly, an improved YOLOv3 model [32] achieved 93% mAP for PCB component detection. Despite these improvements, the model's computational complexity is unsuitable for real-time applications. Nevertheless, the authors highlighted the potential of using synthetic data for training the model, which aligns with our approach. In a transition to lightweight models, a more efficient network was introduced by [37], achieving 85.8% mAP. However, this model was limited by its focus on only a few components and relied on cropped images which restricted its ability to detect all PCB components in a single forward pass. Similarly, a VGG-16-based model [27] was proposed to detect 25 components, but for missing component detection, it only focused on resistors. Similar papers [25,38] focused on component detection without simultaneously addressing defect detection. Misalignment detection has been explored in methods like feature extraction with region optimization [28], which can identify issues such as missing components, no-solder defects, and shifts or rotation, specifically for resistors and capacitors. However, it relies on an expensive RGB illumination system, which is not feasible for our cost-effective system design. Another approach involves a traditional machine learning (ML) method of support vector machine for predicting component shift in SMT [39]. For missing component detection, traditional techniques, such as a hybrid genetic algorithm [40], have been proposed, yet these methods did not differentiate between component types. Additionally, an ML-driven approach combining image processing techniques with color and texture analysis [41] was proposed to enhance PCB component detection. As object detection models continued to evolve, more recent work demonstrated the potential of YOLOv8 for improved PCB component recognition [25]. By employing geometrical and morphological transformations to generate additional training data, this model achieved 87.7% mAP@50 while maintaining an impressive inference speed of 110 FPS.

The aforementioned studies provide valuable references for electronic component detection, but they either concentrate on a specific

aspect with a limited number of components or have limited performance, either due to cost or the algorithm. Moreover, to the best of our knowledge, no prior work has addressed the normal, missing, and misaligned component detection within a unified framework that addresses the unique challenges posed by Arduino boards due to their open-source nature and diverse manufacturing practices.

### 2.2. PCB defect detection

In addition to component detection, detecting defects in circuitry is crucial to ensuring the functionality of PCBs. One of the flagship works in this area, Tiny Defect Detection Network (TDD-Net) [4], introduced an improved Faster R-CNN to identify common PCB defects, including open and short circuits, mouse bites, missing holes, spurious copper, and spurs on the back side, although the model is rather computationally expensive. The TDD-PCB dataset is proposed in the corresponding paper and has been widely used by researchers to develop better detection models, mostly based on YOLO for faster speed and better mAP. Several recent studies that proposed improved YOLOv7 [42] models and tested on this dataset, such as BEW-YOLO [43] and CDI-YOLO [44], both achieving mAP@50 scores above 98%. However, these advances come at a high computational cost, with BEW-YOLO requiring 12.6 GFLOPs (Giga Floating-Point Operations), potentially limiting real-time performance on lower-end hardware. YOLOv7 also lags behind newer baselines in terms of speed.

Further efforts include improving YOLOv8 [45], which achieved 96.6% mAP@50. However, it also has higher GFLOPs, which compromises its real-time efficiency. YOLOv5, known for its balance of speed and accuracy, remains widely used in industry [46]. For instance, FCD-YOLO [47], built on YOLOv5s, achieved 92% mAP@50 for PCB defect detection but at the cost of doubling the parameters. YOLO-HMC [12] was then proposed for PCB defects based on improved YOLOv5s, incorporating HorNet and an attention mechanism while replacing traditional upsampling with Content-Aware Reassembly of Features (CARAFE) [48] in the neck. This model achieved a high 98.6% mAP@50 and 54.9% mAP@50-95, with reduced parameters. However, many models built on YOLOv5s (small) come with inherently more parameters than the nano variant, which is more suitable for resource-constrained hardware. Hybrid approaches, such as PCB-YOLO [24] and Transformer-YOLO [49] integrated Swin-Transformer [50] with YOLOv5 to boost detection performance. While these methods improved mAP, they also substantially increased computational complexity, making real-time processing requirement more challenging to accomplish.

These studies provide valuable references of deep learning for PCB defect detection which is of great importance in our work as the same types of circuitry defects can occur on the backside of Arduino boards and we will be using the same datasets as these works in our work too. Moreover, many studies propose improvements by either prioritizing accuracy at the expense of high computational costs [12,24,43,44,49] or increasing speed while slightly sacrificing accuracy [22,51-53]. To address these challenges, we build upon the YOLOv8n (nano) baseline, optimizing it with various fine-tuned blocks backed by ablation studies for enhanced real-time performance while simultaneously improving the detection accuracy.

### 2.3. Learning from synthetic data

Training on synthesized data often struggles with performance issues because of the domain gap between synthetic and real-world data [54]. In computer vision, synthetic images and videos produced using rendering tools or generative networks [26,30] hold great promise for boosting model performance. For example, InsaGen [55] showed how generative networks could create annotated images for arbitrary categories, improving object detection capabilities. A recent study on quality inspection using deep learning [31] explored how
factors like camera settings, lighting, and noise affect binary defect classification. The findings underscored how crucial environmental conditions are for model performance. Similarly, combining Transformers with data augmentation has been shown to enhance models trained on synthetic data. This approach was particularly effective when using video games, to train networks for real-world drone imagery [30]. In another case, researchers working with industrial metal objects found that data augmentation and transfer learning significantly improved model performance. These techniques allowed models to learn from synthetic data and successfully apply their knowledge to real-world scenarios [56].

In PCB-related applications, one study demonstrated the use of synthesized and augmented data to train a neural network for predicting solder joint reliability [57], while another employed YOLO for electronic component detection using synthesized data [32]. A recent study [58] on solder defects detection in electronic components applied cycle generative adversarial network (Cycle-GAN) for data generation, which is analogous to creating synthetic data, and then ingetrating Swin Transformer [59] with Mask R-CNN [60] to create an improved model. However, these studies relied more on synthetic data, which limited their insights into the generalizability of synthetic data to real-world scenarios. Another recent study [26] investigated the feasibility of using synthetic training data to classify capacitors and resistors, both with real-world and synthesized test data. This study showcased the potential of synthetic-to-real generalization. Although our research aligns with this concept, it is worth noting that this study is limited to three classes of components (resistors and two types of capacitors), and employed an unmodified YOLOv5 backbone [46], resulting in suboptimal and less robust accuracy of 75% that could have benefited from further backbone enhancements. More recently, a study [61] on PCB defect detection used YOLOv5 as a baseline and explored synthetic-to-real generalization due to the scarcity of X-ray PCB datasets. The findings showed that a synthesized X-ray PCB dataset could generalize well to real X-ray images with actual defects, especially when enhanced with a domain adaptation framework. These results validate the feasibility of learning from synthesized data for industrial vision applications.

# 3. Methodology

## 3.1. Datasets

We used datasets for both component and circuitry defects in order to address quality assurance and defect detection using deep learning. We further validated the efficacy of our model for detecting backside circuitry defects using the DeepPCB dataset [62] and the well-known TDD-PCB dataset [4], which is also synthetic and commonly used in pertinent research. Fig. 2 shows the six different kinds of defects.

For electronic component detection, a dataset was built from a 3D model, available on GrabCAD [63]. Several studies utilizing synthetic training have employed Blender [26,31], and the 3D model used in this work was created using SolidWorks, a similar software. The original synthesized Arduino board is shown in Fig. 3(a) with annotations. Various image patterns were generated by adjusting brightness and component shifts, with Fig. 3(b) illustrating misalignment samples. Data augmentation, including rotation, brightness, and noise adjustments, expanded the dataset (Fig. 3(c)), as camera settings, noise, and lighting conditions would affect the defect detection performance with synthetic training [31]. There are 21 total classes, and missing components were treated as a separate dataset due to limited data, to avoid overloading the model with a doubled number of classes, which would risk reducing performance. Fig. 3(d) displays an annotated image from the missing component dataset.

The misalignment dataset contains 2805 images, generated by cropping components and applying augmentations. Fig. 4 shows examples,

with the first row displaying synthesized components and the second row displaying test data from real Arduino board images. The component detection dataset comprises 2495 images, with synthesized training data and a test set where half the images come from real boards. The missing component dataset includes 479 entirely synthesized images, due to challenges in sourcing real images with missing components. For PCB defects, the TDD-PCB dataset [4] has 10,668 images, while DeepPCB dataset [62] contains 1500. The datasets are split into training, validation, and test sets with a 70%:15%:15% ratio.

## 3.2. Overall system design

Fig. 5 shows the entire system. The model training and deployment procedure is described in Fig. 5(a), where the AI model is first trained after the synthesis of the training set, and depending on the hardware (CPU/GPU/NPU) for deployment, the model is quantized and the computation graph is optimized after training. The model can make predictions on test data in real-time after it has been deployed. Similar steps are used by the circuitry defect detection model, which uses the same model architecture and the model is trained on the TDD-PCB dataset. The switch between front-side and back-side defect detection mode simply involves updating the corresponding pre-trained weights, which is managed by our software system which will be presented later in Section 4.6.

Fig. 5(b) shows the pipeline for inference: The user forwards a test image to our proposed CGTA-YOLO-F model (detailed in Section 3.3), which detects components and missing components in parallel threads using the corresponding weights. After detection, the identified components pass through the isolated backbone of the model, followed by a convolutional layer, average pooling, and flattening layer that connects to two neurons via a dense layer, to assess whether the components are correctly placed or misaligned as a binary classification problem. This unified framework eliminates the need for a different and additional classifier for misalignment detection. The performance comparison of the backbone as an image classifier with other convolutional or transformer-based classifiers will be detailed in Section 4.3.3.

## 3.3. The proposed model: CGTA-YOLO-F

We introduce CGTA-YOLO-F, a novel framework designed for better generalizability for learning from synthesized data and accelerated deployment across various platforms using hardware acceleration techniques. The “F” signifies both the hardware acceleration for faster inference and our custom C2f-FCGA block, inspired by the style of FasterNet [64] (detailed in Section 3.5). CGTA-YOLO-F is built upon the YOLOv8 [20] baseline, one of the SOTA anchor-free object detection models with superior speed and accuracy.

YOLOv8 uses ConvBNSiLU (Convolution, Batch Normalization, SiLU activation) to downsample and extract features. Based on the Cross Stage Partial Network (CSP) [65] and Efficient Long-range Attention Network (ELAN) [66] idea, the C2f block, which is depicted in the lower left corner of Fig. 6, has a bottleneck structure with two ConvBNSiLU blocks that have residual connections. This approach improves gradient flow information while keeping the model lightweight. On the left side of Fig. 6, the Spatial Pyramid Pooling-Fast (SPPF) [67] incorporates three max-pooling blocks that are concatenated to capture features at various scales.

The Neck utilizes Feature Pyramid Network (FPN) [68] and Path Aggregation Network (PAN) [69] in a bi-directional manner to propagate high-level features to low-level ones, enriching semantic information. C2f with an upsampling layer enables feature fusion by expanding feature maps for smaller objects. In order to improve computational efficiency, YOLOv8 employs three feature maps of different scales, each detection head using Varifocal Loss (VFL) for classification, Distribution Focal Loss (DFL) [70] in conjunction with Complete Intersection over
<figure><img src="image_5.png" /><figcaption>Fig. 2. Preview of the TDD-PCB and DeepPCB datasets with annotated ground truth labels: (a) Short; (b) Open; (c) Missing hole; (e) Spurious copper; (f) Spur; (g) Mouse bite; (h) DeepPCB example 1; (i) DeepPCB example 2.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 3. Dataset samples: (a) Original annotated synthesized Arduino board with no missing or misaligned components; (b) Manually generated misaligned components; (c) Example of data augmentation — bounding box level rotation, brightness change, and noise; (d) Missing components annotation.</figcaption></figure>

Union (CIoU) [71] for bounding box regression, and an anchor-free model design.

Multi-scale feature extraction and feature fusion are carried out in a similar manner to other object detection models, where the input image (resolution *F* = 1024 × 1024) is first downsampled by the backbone to create feature maps from *P*₁(512) to *P*₅(32), where *P*ᵢ denotes a resolution of *F*², for instance, *P*₄ = 64. Fig. 6 also provides a clear representation of the feature map sizes at each layer's output. layer's output.

A top-down FPN [68] makes up the neck, which aggregates multi-scale features using the *P*₃ to *P*₅ stages. To further refine feature fusion, an additional bottom-up PAN [69] is implemented, concatenating the feature maps in the same stages from FPN. Fig. 7 demonstrates the concept with feature map sizes and Class activation Maps (CAM) which are attention heat map visualizations shown in the image to better visually illustrate how the model (Fig. 6) processes the input image.

Nevertheless, as we will explain in Section 4, YOLOv8’s performance is still not ideal when trained exclusively on synthesized data. To tackle
<figure><img src="image_7.png" /><figcaption>Fig. 4. Sample data images of misaligned components generated from data augmentation. (a)–(e): synthesized training data; (f)–(j): real test data.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 5. Overall system design.</figcaption></figure>

this, we suggest improving feature extraction and feature fusion, with enhancements described in depth in the sections that follow.

## 3.4. Backbone improvement

The Context Guided Block was introduced in CGNet [72]. CGB extracts joint features by combining local features with the surrounding context by incorporating global context to effectively fuse the extracted information. As shown in Fig. 8, $f_{loc}(*)$ denotes the local feature extractor, $f_{sur}(*)$ the surrounding context extractor, $f_{joi}(*)$ the joint feature extractor, and $f_{glo}(*)$ the global context extractor. Residual learning is used, with Local Residual Learning (LRL) connecting the input to the joint extractor and Global Residual Learning (GRL) linking the input to the global context extractor. This combination balances intricate local feature extraction with broader global context.

The joint features are represented as:

$$ \hat{f}_{joi} = f_{joi}(\hat{f}_{loc}, \hat{f}_{sur}) = \text{PReLU}(\text{BatchNorm}([\hat{f}_{loc}, \hat{f}_{sur}])) \quad (1) $$

The global context features are obtained through a global average pooling layer, followed by two fully connected layers, where $\sigma$'s represent the activation functions:

$$ \hat{f}_{glo} = f_{glo}(\hat{f}_{joi}) = \sigma_s(\text{Dense}(\sigma_r(\text{Dense}(\text{GAP}(\hat{f}_{joi})))) \quad (2) $$

The output features are then computed, where $\odot$ represents element-wise multiplication:

$$ \hat{f}_{out} = \hat{f}_{joi} \odot \hat{f}_{glo} \quad (3) $$

Originally designed for lightweight semantic segmentation on mobile devices, the CGB's application in integration with YOLO for defect detection is limited in existing research. YOLOv8, despite its SOTA performance, faces challenges in complex scenes and small object detection, often neglecting contextual information. In electronic component placement, surrounding context and lighting variations can impact detection accuracy, especially for varying-style Arduino boards, illustrated earlier in Fig. 1. The goal of YOLOv8's CGB replacement of ConvBNSILU layers is to increase detection robustness and accuracy by effectively extracting both object and contextual information with an efficient level of computational complexity.

The latest YOLOv10 [22], which also builds on YOLOv8, has a number of improvements. These include the C2f-CIB block (compact inverted block), which uses depthwise convolutions for spatial mixing and efficient pointwise convolutions for channel mixing, the PSA at the backbone's end to improve feature extraction, and the innovation of post-processing-free operation for faster training and inference. PSA
<figure><img src="image_9.png" /><figcaption>Fig. 6. Architecture of our proposed model CGTA-YOLOv8: the key changes compared to YOLOv8 are highlighted in the red boundary. Feature map sizes are denoted in $h \times w \times c$ format, where *h* is height, *w* denotes weights, and *c* signifies channels.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 7. Visualization of feature maps with CAM heatmaps (P1-P5).</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 8. Overview of the Context Guided Block [72].</figcaption></figure>

is designed to reduce computational complexity compared to traditional multi-head self-attention [9], offering a more efficient approach. However, PSA still requires 249,728 parameters on our dataset. We opted for the fully convolutional triplet attention [34], which has only 200 parameters, achieving a 99% reduction in the total parameters of the attention mechanism compared to PSA, while having higher

performance in our specific application. In contrast to other convolutional attention mechanisms like Squeeze-and-Excitation Networks (SE-Net) [73] and Convolutional Block Attention Module (CBAM) [74], which may lose spatial information, TA introduces the concept of cross-dimension interaction using three parallel branches. As depicted in Fig. 9, the first two branches capture the cross-dimension interaction
<figure><img src="image_12.png" /><figcaption>Fig. 9. Illustration of Triplet Attention block: channel-width (cw), channel-height (ch), and height-width (hw). The red-shaded area corresponds to the Simplified Attention as a sub-block of the FCGA bottleneck (detailed later).</figcaption></figure>

between channel (C) and either spatial height (H) or width (W), while the last branch is somewhat analogous to CBAM for spatial attention. The outputs of the three branches are then averaged.

The Z-pool layer reduces the zeroth dimension of the tensor to two by concatenating average-pooled and max-pooled features. This preserves a rich representation of the tensor while minimizing depth for efficient computation, and is formulated as:

$$Z-pool(\chi) = [MaxPool_{0d}(\chi), AvgPool_{0d}(\chi)] \qquad (4)$$

where $\chi \in \mathbb{R}^{C \times H \times W}$ denotes the input tensor, 0d refers to the 0th dimension, and the dimension is reduced to $(2 \times H \times W)$ through MaxPool and AvgPool.

The refined tensor, denoted as $y$, resulting from the application of Triplet Attention to an input tensor $\chi$, can be simplified by the equation:

$$y = \frac{1}{3}(\overline{\chi_1\omega_1} + \overline{\chi_2\omega_2} + \chi\omega_3) \qquad (5)$$

where $\omega$'s represents the three cross-dimensional attention weights computed in Triplet Attention, and the bar denotes a 90° clockwise rotation to preserve the original input shape.

The use of TA also facilitates the efficient design of simplified attention through the novel FCGA bottleneck, as detailed in the following subsection. Its more compact integration also optimizes the model's integration for deployment.

### 3.5. Neck improvement

In object detection models, the backbone is primarily responsible for feature extraction, whereas the neck facilitates feature fusion across multiple scales. Two ConvBNSiLU blocks with a 3 × 3 kernel, stride of 1, and padding of 1 make up the bottleneck used in the neck of the original YOLOv8 and YOLOv10 architectures, which use the C2f module. Despite its effectiveness, this structure can be further refined for situations that call for a greater level of computing efficiency and spatial context awareness. In order to do this, we propose a novel C2f-FCGA module that integrates our FCGA bottleneck. It is different from the original C2f and FasterNet [64] blocks in a number of ways, as illustrated in Fig. 10.

The FCGA bottleneck's design philosophy is first inspired by Faster-Net, but with key adaptations for object detection. Partial convolution proved suboptimal in our application as it does not adequately capture multi-scale dependencies for complex feature fusion. Instead, we applied CGB to the bottleneck, which strengthens how local and global features interact. The CGB uses feature-gating methods to encode global context and a combination of dilated convolutions and standard convolutions for local feature extraction. The neck may more precisely concatenate and fuse features from various backbone stages.

<figure><img src="image_13.png" /><figcaption>Fig. 10. Comparison of C2f bottleneck: (a) Original; (b) FasterNet; (c) FCGA (ours).</figcaption></figure>

Furthermore, we propose a simplified attention mechanism derived from the triplet attention, retaining only the channel-width (cw) attention, as shaded in red in Fig. 9. This reduction streamlines the attention mechanism, focusing solely on enhancing channel interactions while discarding spatial and height-based correlations. This simplification improves computational efficiency without sacrificing a better focus on key feature dependencies in the neck of the model, avoiding over-attention at each stage throughout the neck. The simplified attention enhances channel interactions while maintaining strong performance in scenarios where channel-wise correlations dominate.

To further optimize performance, we replaced the Rectified Linear Unit (ReLU) activation used in FasterNet with Sigmoid Linear Unit (SiLU) activation [75], which offers smoother gradient propagation, facilitating improved convergence and overall performance in complex tasks like object detection. Layer scaling is also employed in the FCGA bottleneck to stabilize training in deeper architectures by balancing feature contributions across layers. This addition prevents the over-amplification of features and ensures a more uniform gradient flow throughout the network.

The FCGA bottleneck targets the C2f blocks in the neck, where features from multiple scales and resolutions converge. While backbone networks excel at initial feature extraction, premature over-attention or complex feature manipulation can potentially obscure critical spatial information. By introducing local and global feature interactions at the neck, we enable better feature fusion without compromising the backbone's primary extraction capabilities.

Section 4.4 will compare the models with the original bottleneck and the FCGA bottleneck to study the performance gain, with a second ablation study diving further into the construction of the new bottleneck, by studying the difference of the enhancements.
<table><caption>**Table 1**
Platform configuration.</caption><thead><tr><td>Component</td><td>Specification</td></tr></thead><tbody><tr><td>GPU</td><td>2 x NVIDIA A100 80 GB PCIe</td></tr><tr><td>CPU</td><td>Intel® Xeon® Gold 6300 28-Core 2 GHz</td></tr><tr><td>RAM</td><td>2 TB</td></tr><tr><td>Operating system</td><td>Ubuntu 24.04</td></tr><tr><td>Python</td><td>3.9.19</td></tr><tr><td>PyTorch</td><td>2.0.1</td></tr><tr><td>CUDA</td><td>11.8</td></tr></tbody></table>

# 4. Results & discussion

In this section, we conduct a comparative analysis between various models and our proposed method.

## 4.1. Evaluation metrics

For the defect detection task, we evaluate our models using common metrics for object detection: precision, recall, and mean average precision (mAP). For the misalignment classification task, we assess model performance using accuracy. The calculations are based on the values of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) to compute the following metrics:

$$ \text{Accuracy (Acc.)} = \frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}} \qquad (6) $$

$$ \text{Precision (P)} = \frac{\text{TP}}{\text{TP} + \text{FP}} \qquad (7) $$

$$ \text{Recall (R)} = \frac{\text{TP}}{\text{TP} + \text{FN}} \qquad (8) $$

$$ \text{Average Precision (AP)} = \int_{0}^{1} p(r), dr \qquad (9) $$

$$ \text{mean Average Precision (mAP)} = \frac{1}{k} \sum_{i=1}^{k} AP_{i} \qquad (10) $$

By averaging over the AP values across all k classes, we obtain a scalar value mAP which expresses the overall performance of the model.

## 4.2. Platform configuration and model training

To train the models, we used two NVIDIA A100 GPUs. The hardware specifications and software versions are summarized in Table 1. We employed the AdamW optimizer [76] with a learning rate of 0.0004 and a momentum of 0.9, trained for 200 epochs to ensure full convergence. The best model was selected based on validation set performance. For misalignment classification, we isolated and trained the backbone separately, using a learning rate of 0.000714 while keeping the rest of the hyperparameters unchanged. In addition to dataset generation with data augmentation techniques, training-time augmentations and transfer learning are also applied to improve synthetic training performance, with is also supported by several studies in the related work section [30,56]. We applied random flipping, scaling, translation, mosaic [77], and jittering [78] during training to further enhance the model training. All models utilized transfer learning from pre-trained YOLOv8 weights on the COCO dataset, with the baseline initialized with full weights except for the head layers, while other models loaded pre-trained weights for the unchanged parts relative to YOLOv8.

<table><caption>**Table 2**
Performance comparison of models for the component detection task on the Arduino dataset.</caption><thead><tr><td>Model</td><td>mAP<sub>50</sub></td><td>mAP<sub>50:95</sub></td><td>#Param.</td><td>GFLOPs</td><td>ms/img</td></tr></thead><tbody><tr><td>Faster R-CNN [8]</td><td>86.9%</td><td>75.4%</td><td>41.5M</td><td>134.4</td><td>62.5</td></tr><tr><td>YOLOv5n [46]</td><td>93.7%</td><td>77.7%</td><td>2.5M</td><td><strong>7.1</strong></td><td>13.4</td></tr><tr><td>YOLOv8n [20]</td><td>94.4%</td><td>77.8%</td><td>3.0M</td><td>8.1</td><td>12.1</td></tr><tr><td>YOLOv9t [21]</td><td>95.6%</td><td>78.9%</td><td><strong>2.0M</strong></td><td>7.6</td><td>22.0</td></tr><tr><td>YOLOv10n [22]</td><td>94.0%</td><td>77.3%</td><td>2.7M</td><td>8.3</td><td><strong>7.9</strong></td></tr><tr><td>YOLO11n [79]</td><td>94.9%</td><td>78.4%</td><td>2.6M</td><td>6.5</td><td>11.3</td></tr><tr><td>RT-DETR [11]</td><td>95.0%</td><td><strong>79.1%</strong></td><td>32.0M</td><td>103.5</td><td>39.2</td></tr><tr><td>CGTA-YOLO-F (Ours)</td><td><strong>97.4%</strong></td><td>77.9%</td><td>3.6M</td><td>9.3</td><td>14.8</td></tr></tbody></table>

## 4.3. Experiment results and model comparison

To evaluate our proposed model's performance and generalization capabilities, we compared it to several SOTA object detection models, including the latest YOLO variants: YOLOv8 [20], YOLOv9 [21], and YOLOv10 [22], as well as YOLOv5 [46], which is still widely used in industrial applications. Additionally, we compared our model with RT-DETR [11] to test its performance against SOTA transformer-based architecture for real-time object detection.

Three different datasets were evaluated, with special attention paid to comparisons with YOLOv8 and YOLOv10. This comparison enables us to emphasize the clear benefits provided by our use of TA in our application, since YOLOv8 is our baseline and YOLOv10 is a recent SOTA model with the novel PSA integrated into the backbone, which is missing in YOLOv8. YOLO11 [79], which does not include the version letter “v” in its name, is an improved version built upon YOLOv8 and is flagged as v8.3 in the official repository, proposed by the same team. YOLO11 focuses on model efficiency and further enhances components from YOLOv10, such as integrating PSA into C2 blocks and replacing traditional convolution with depthwise convolution (DWConv) in the decoupled head, resulting in a more lightweight and computationally efficient model. Nevertheless, it still relies on NMS, which does not offer the same level of efficiency as YOLOv10.

Model explainability is crucial in demonstrating the incorporated enhancements. To this end, we utilized Eigen-CAM [80] to generate heatmaps on selected test samples, enabling visualization of the model's attention on regions of interest. This interpretability further substantiates the effectiveness of our approach in improving the model's focus and detection capabilities.

### 4.3.1. Model performance comparison: Arduino components

We begin by focusing on Arduino component detection, which is the primary objective of this work. Our goal is to create a dataset through synthesis which can easily cover more variations and defects and then generalize it to real-world test data. As illustrated in Figs. 4 and 11, the test data comprises a diverse range of data from actual Arduino boards, exhibiting varying components. Table 2 summarizes the collected statistics. The inference speed using the raw PyTorch model was measured in milliseconds per image (ms/img) on an NVIDIA A100 GPU. Our results show that the two-stage detector, Faster R-CNN, delivers lower mAP, while also suffering from significantly higher FLOPs and slower speed. Surprisingly, YOLOv9t achieved the highest 95.6% mAP among the baselines, with a low parameter count. However, inference speed is not directly impacted by the number of parameters and is also related to its architecture and YOLOv9 is the slowest compared to other models in this task, running at 22 ms/img. In contrast, YOLOv5n, with slightly more parameters but lower GFLOPs delivered a much faster speed of 13.4 ms/img, albeit with a notable mAP drop to 93.7%. YOLOv8, featuring an anchor-free design, achieved a solid balance with a mAP of 94.4% and a fast speed of 12.1 ms/img, striking the balance.

Earlier YOLO iterations relied on NMS to remove redundant bounding boxes. YOLOv10 employs dual label assignments and removes post-processing to be the fastest baseline with an inference time of 7.9
<figure><img src="image_14.png" /><figcaption>**Fig. 11.** Predictions and CAM heatmaps on sample test data of two real Arduino boards for model comparison. YOLOv8 and YOLOv10 both have a multitude of false negatives where as YOLOv10 has higher attention on detected components. Our proposed method, CGTA-YOLO-F has more significantly reduced false negatives while having more centralized attention on detections.</figcaption></figure>

<table><caption>**Table 3**
Performance comparison of models for the missing component detection task on the Arduino dataset.</caption><thead><tr><th>Model</th><th>mAP<sub>50</sub></th><th>mAP<sub>50:95</sub></th><th>#Param</th><th>GFLOPs</th><th>ms/img</th></tr></thead><tbody><tr><td>Faster R-CNN [8]</td><td>86.2%</td><td>72.3%</td><td>41.5M</td><td>134.4</td><td>63.4</td></tr><tr><td>YOLOv5n [46]</td><td>98.2%</td><td>78.1%</td><td>2.5M</td><td><b>7.1</b></td><td>8.0</td></tr><tr><td>YOLOv8n [20]</td><td>98.2%</td><td>80.0%</td><td>3.0M</td><td>8.1</td><td>7.1</td></tr><tr><td>YOLOv9t [21]</td><td>98.2%</td><td>78.0%</td><td><b>2.0M</b></td><td>7.6</td><td>11.8</td></tr><tr><td>YOLOv10n [22]</td><td>97.2%</td><td>78.7%</td><td>2.7M</td><td>8.3</td><td><b>6.9</b></td></tr><tr><td>YOLO11n [79]</td><td>98.2%</td><td>79.9%</td><td>2.6M</td><td>6.4</td><td>7.3</td></tr><tr><td>RT-DETR [11]</td><td>98.3%</td><td><b>80.9%</b></td><td>32.0M</td><td>103.5</td><td>32.3</td></tr><tr><td>CGTA-YOLO-F (Ours)</td><td><b>98.6%</b></td><td>79.9%</td><td>3.6M</td><td>9.3</td><td>11.6</td></tr></tbody></table>

ms/img for component detection. However, its 94.0% mAP is slightly lower than YOLOv8. While YOLO11 achieves a 94.9% mAP on the Arduino component dataset and is 0.5% higher than YOLOv8, it still depends on NMS post-processing, making it less efficient. Additionally, its mAP improvement is marginal on the other datasets, and in some cases, it performs worse than YOLOv8, as will be discussed in later subsections.

RT-DETR incorporates an efficient hybrid encoder and an uncertainty-minimal query selection mechanism, advancing DETRs into real-time applications [11]. On the component detection task, it has stronger performance than YOLOv8 and YOLOv10 with 95.0% mAP@50. However, RT-DETR's high parameter count of 32M places it between YOLOv8 m and YOLOv8l, and significantly above YOLOv8n, making it less suitable for lightweight deployment scenarios. Furthermore, with the raw PyTorch model, its runs at 39.3 ms/img even on a high-end GPU, which is noticeably slower and limits its practicality for deployment in resource-constrained Arduino factories which require more model efficiency and compactness.

Our proposed model, CGTA-YOLO-F, outperforms all other YOLO baselines, achieving 97.4% mAP, which is 3% higher than YOLOv8 and 3.4% higher than YOLOv10. Although CGTA-YOLO-F has 0.6M more parameters than YOLOv8 with an inference speed of 14.8 ms/img (68 FPS), the slight reduction in speed is imperceptible in practical use and meets real-time requirements. In Section 4.5, we will demonstrate that with hardware acceleration, the model can achieve real-time performance even on mobile processors. The results for missing component detection are presented in Table 3. The performance gains in this task are less pronounced, due to the limitations of the synthesized test set and the lack of real board images with diverse missing component configurations. Nevertheless, our model still delivers a 0.4% higher mAP@50, with other parameters remaining comparable. To further

substantiate these findings, we compare the class-level AP of CGTA-YOLO-F with YOLOv8 and YOLOv10, as detailed in Table 4. The class AP is consistently higher for CGTA-YOLO-F across most components, with the exception of the 16 MHz crystal, GPIO ports, and resistors that have slightly lower AP.

We also generate CAM heatmaps shown in Fig. 11. Using test data from real Arduino boards, we compare predictions from our proposed method with those from SOTA object detection models. As seen, YOLOv8 produces a significant number of false negatives, failing to detect many components. YOLOv10, while also missing some detections, exhibits stronger attention on the detected components, thanks to the integrated attention mechanism in its backbone. In contrast, CGTA-YOLO-F, with the CGTA backbone and an enhanced C2f bottleneck incorporating simplified attention in the neck, shows superior performance, with significantly fewer false negatives and more centralized attention on the detected components. The attention is more organized and focused in the center of the components, as clearly illustrated in the figures in the last column of Fig. 11. Further details on the impact of these design choices of CGTA-YOLO-F will be discussed in the ablation study (Section 4.4.2).

Overall, the proposed model exhibited superior generalizability when trained entirely on synthesized data and subsequently evaluated on different real test samples, demonstrating superior performance in addressing the challenges associated with style variability in defect detection compared to existing object detection models.

### 4.3.2. Model performance comparison: PCB defects

We also test our model on two related additional PCB defects datasets: TDD-PCB [4] and DeepPCB [62], to further evaluate and prove the effectiveness of the proposed model.

The results on the TDD-PCB dataset are presented in Table 5. YOLOv8 outperformed previous YOLO iterations, with 98.1% mAP@50 and 56.4% mAP@50-95. YOLOv10 followed closely, providing the best balance of accuracy and speed, whereas YOLOv9 lagged in mAP@50-95 and speed. Despite being faster, YOLO11 has slightly lower mAP@50 (97.8%) than YOLOv8. Our CGTA-YOLO-F outperformed them, obtaining 98.5% mAP@50 (a 0.4% and 0.6% increase, respectively) and 59.4% mAP@50-95 (a 3% and 5% improvement). The mAP@50-95 metric is a more comprehensive evaluation that requires larger overlap at higher Intersection over Union (IoU) thresholds, indicating how closely predicted bounding boxes match ground truth. Contrary to component detection, this is particularly important for PCB defects, which are often small and pose substantial risks to system functionality. Accurate localization, as demonstrated by higher mAP@50-95 scores,
<caption>**Table 4**</caption>

Class-level AP comparison of state-of-the-art models on the Arduino component dataset.

<table><caption>**Table 5**</caption><thead><tr><th>Class</th><th>YOLOv8n</th><th>YOLOv10n</th><th>CGTA-YOLO-F (Ours)</th></tr></thead><tbody><tr><td>16 MHz Crystal</td><td>95.8%</td><td><strong>98.3%</strong></td><td>98.2%</td></tr><tr><td>47 uF Capacitor</td><td>99.5%</td><td>99.5%</td><td><strong>99.5%</strong></td></tr><tr><td>ATMEL328P</td><td>92.4%</td><td>97.9%</td><td><strong>98.9%</strong></td></tr><tr><td>ATMega16u2</td><td>99.0%</td><td>99.5%</td><td><strong>99.5%</strong></td></tr><tr><td>CD1206</td><td>98.9%</td><td>98.8%</td><td><strong>99.5%</strong></td></tr><tr><td>Capacitor</td><td>83.7%</td><td>86.2%</td><td><strong>89.8%</strong></td></tr><tr><td>Crystal</td><td>97.7%</td><td>98.7%</td><td><strong>99.5%</strong></td></tr><tr><td>Fuse</td><td>99.5%</td><td>99.5%</td><td><strong>99.5%</strong></td></tr><tr><td>LED</td><td>92.2%</td><td>86.0%</td><td><strong>93.5%</strong></td></tr><tr><td>LMV358</td><td>98.2%</td><td>93.1%</td><td>99.4%</td></tr><tr><td>M7 diode</td><td>99.5%</td><td>99.4%</td><td><strong>99.5%</strong></td></tr><tr><td>PIN Header</td><td>88.3%</td><td>88.1%</td><td><strong>99.5%</strong></td></tr><tr><td>Ports</td><td><strong>96.7%</strong></td><td>91.7%</td><td>94.8%</td></tr><tr><td>Power Jack</td><td>88.7%</td><td>96.0%</td><td><strong>99.5%</strong></td></tr><tr><td>Regulator</td><td>95.5%</td><td>96.2%</td><td><strong>99.5%</strong></td></tr><tr><td>Resistor Pack</td><td>98.2%</td><td>96.8%</td><td><strong>98.7%</strong></td></tr><tr><td>Reset</td><td>93.3%</td><td>94.7%</td><td><strong>95.6%</strong></td></tr><tr><td>Resistor</td><td>84.2%</td><td><strong>85.7%</strong></td><td>82.6%</td></tr><tr><td>SSOT3</td><td>83.6%</td><td>71.6%</td><td><strong>99.5%</strong></td></tr><tr><td>USB Port</td><td>99.5%</td><td>99.5%</td><td><strong>99.5%</strong></td></tr><tr><td>LP2985</td><td>97.8%</td><td>96.5%</td><td><strong>99.5%</strong></td></tr><tr><td>All classes mAP</td><td>94.4%</td><td>94.0%</td><td><strong>97.4%</strong></td></tr></tbody></table>

Performance comparison of models for the PCB defect detection task on TDD-PCB dataset.

<table><caption>**Table 6**</caption><thead><tr><th>Model</th><th>mAP<sub>50</sub></th><th>mAP<sub>50:95</sub></th><th>#Param.</th><th>GFLOPs</th><th>ms/img</th></tr></thead><tbody><tr><td>Faster R-CNN [8]</td><td>87.2%</td><td>40.1%</td><td>41.5M</td><td>134.4</td><td>49.8</td></tr><tr><td>YOLOv5n [46]</td><td>97.9%</td><td>54.3%</td><td>2.5M</td><td><strong>7.1</strong></td><td>7.0</td></tr><tr><td>YOLOv8n [20]</td><td>98.1%</td><td>56.4%</td><td>3.0M</td><td>8.1</td><td>7.2</td></tr><tr><td>YOLOv9t [21]</td><td>97.7%</td><td>52.5%</td><td><strong>2.0M</strong></td><td>7.6</td><td>13.5</td></tr><tr><td>YOLOv10n [22]</td><td>97.9%</td><td>54.4%</td><td>2.7M</td><td>8.2</td><td><strong>6.9</strong></td></tr><tr><td>YOLO11n [79]</td><td>97.8%</td><td>54.8%</td><td>2.6M</td><td>6.4</td><td>7.0</td></tr><tr><td>RT-DETR [11]</td><td>97.3%</td><td>53.2%</td><td>31.9M</td><td>103.5</td><td>29.0</td></tr><tr><td>CGTA-YOLO-F (Ours)</td><td><strong>98.5%</strong></td><td><strong>59.4%</strong></td><td>3.6M</td><td>9.3</td><td>12.1</td></tr></tbody></table>

Performance comparison of models for the PCB defect detection task on DeepPCB dataset.

<table><caption>**Table 7**</caption><thead><tr><th>Model</th><th>mAP<sub>50</sub></th><th>mAP<sub>50:95</sub></th><th>#Param.</th><th>GFLOPs</th><th>ms/img</th></tr></thead><tbody><tr><td>Faster R-CNN [8]</td><td>94.2%</td><td>68.2%</td><td>41.5M</td><td>134.4</td><td>52.3</td></tr><tr><td>YOLOv5n [46]</td><td>97.5%</td><td>74.3%</td><td>2.5M</td><td><strong>7.1</strong></td><td>8.4</td></tr><tr><td>YOLOv8n [20]</td><td>97.2%</td><td>73.4%</td><td>3.0M</td><td>8.1</td><td>8.5</td></tr><tr><td>YOLOv9t [21]</td><td>97.6%</td><td>71.3%</td><td><strong>2.0M</strong></td><td>7.6</td><td>14.5</td></tr><tr><td>YOLOv10n [22]</td><td>96.2%</td><td>74.7%</td><td>2.7M</td><td>8.2</td><td><strong>7.3</strong></td></tr><tr><td>YOLO11n [79]</td><td>97.2%</td><td>76.2%</td><td>2.6M</td><td>6.4</td><td>8.6</td></tr><tr><td>RT-DETR [11]</td><td>94.6%</td><td>73.3%</td><td>31.9M</td><td>103.5</td><td>36.2</td></tr><tr><td>CGTA-YOLO-F (Ours)</td><td><strong>97.9%</strong></td><td><strong>77.1%</strong></td><td>3.6M</td><td>9.3</td><td>12.6</td></tr></tbody></table>

Class-level AP comparison of state-of-the-art models on TDD-PCB dataset.

<table><thead><tr><th>Model</th><th colspan="2">YOLOv8n</th><th colspan="2">YOLOv10n</th><th colspan="2">CGTA-YOLO-F (Ours)</th></tr><tr><th>Class/Metric</th><th>AP<sub>50</sub></th><th>AP<sub>50:95</sub></th><th>AP<sub>50</sub></th><th>AP<sub>50:95</sub></th><th>AP<sub>50</sub></th><th>AP<sub>50:95</sub></th></tr></thead><tbody><tr><td>Missing hole</td><td>99.5%</td><td>63.2%</td><td>99.3%</td><td>60.4%</td><td><strong>99.5%</strong></td><td><strong>65.0%</strong></td></tr><tr><td>Mouse bite</td><td>98.7%</td><td>57.3%</td><td>98.0%</td><td>53.7%</td><td><strong>99.1%</strong></td><td><strong>60.4%</strong></td></tr><tr><td>Open</td><td>97.8%</td><td>52.7%</td><td>97.8%</td><td>51.4%</td><td><strong>98.5%</strong></td><td><strong>57.3%</strong></td></tr><tr><td>Short</td><td>96.6%</td><td>55.5%</td><td><strong>97.3%</strong></td><td>54.3%</td><td>97.1%</td><td>59.5%</td></tr><tr><td>Spur</td><td>97.4%</td><td>54.1%</td><td>97.1%</td><td>51.9%</td><td><strong>97.7%</strong></td><td><strong>57.1%</strong></td></tr><tr><td>Spurious copper</td><td>98.6%</td><td>54.8%</td><td>98.1%</td><td>54.8%</td><td><strong>99.0%</strong></td><td><strong>56.9%</strong></td></tr><tr><td>All classes mAP</td><td>98.1%</td><td>56.1%</td><td>97.9%</td><td>54.4%</td><td><strong>98.5%</strong></td><td><strong>59.4%</strong></td></tr></tbody></table>

could also enable potential automated repair processes, where precise identification of defect locations is crucial.

Similar trends are evident in the DeepPCB dataset (Table 6), where YOLOv10 exhibited stronger localization performance with the highest mAP@50-95 among baseline models, and YOLOv8 and YOLOv9

<caption>**Table 8**</caption>

Class-level AP comparison of state-of-the-art models on DeepPCB dataset.

<table><thead><tr><th rowspan="2">Model</th><th colspan="2">YOLOv8n</th><th colspan="2">YOLOv10n</th><th colspan="2">CGTA-YOLO-F (Ours)</th></tr><tr><th>AP<sub>50</sub></th><th>AP<sub>50:95</sub></th><th>AP<sub>50</sub></th><th>AP<sub>50:95</sub></th><th>AP<sub>50</sub></th><th>AP<sub>50:95</sub></th></tr></thead><tbody><tr><td>Copper</td><td>98.1%</td><td>85.1%</td><td>98.3%</td><td>89.3%</td><td><strong>98.8%</strong></td><td><strong>89.5%</strong></td></tr><tr><td>Mouse bite</td><td>97.8%</td><td>74.0%</td><td>96.5%</td><td>74.1%</td><td><strong>96.7%</strong></td><td><strong>76.1%</strong></td></tr><tr><td>Open</td><td>98.2%</td><td>67.8%</td><td>94.4%</td><td>65.3%</td><td><strong>99.1%</strong></td><td><strong>71.2%</strong></td></tr><tr><td>Pin-hole</td><td>98.6%</td><td>84.0%</td><td>98.1%</td><td>84.5%</td><td><strong>99.0%</strong></td><td><strong>88.2%</strong></td></tr><tr><td>Short</td><td>93.9%</td><td>61.7%</td><td>91.8%</td><td>61.9%</td><td><strong>95.4%</strong></td><td><strong>64.6%</strong></td></tr><tr><td>Spur</td><td>96.3%</td><td>67.9%</td><td>98.1%</td><td><strong>73.4%</strong></td><td><strong>98.4%</strong></td><td><strong>72.9%</strong></td></tr><tr><td>All classes mAP</td><td>97.2%</td><td>73.4%</td><td>96.2%</td><td>74.7%</td><td><strong>97.9%</strong></td><td><strong>77.1%</strong></td></tr></tbody></table>

showed better mAP@50. On this dataset, YOLO11 has the same detection performance (mAP@50) as YOLOv8 but has a stronger localization performance (76.2%), contrary to the previous dataset. Our CGTA-YOLO-F delivered superior results, reaching 97.9% mAP@50 and 77.1% mAP@50-95, representing gains of 0.7% and 1.7% in mAP@50 compared to YOLOv8 and YOLOv10, and more substantial improvements of 3.7% and 2.4% in mAP@50-95.

As shown in Tables 7 and 8, CGTA-YOLO-F consistently outperforms across class-level detection of the six types of PCB defects, demonstrating its robustness and precision, particularly in detecting smaller, critical defects.

With training and testing on the TDD-PCB and DeepPCB datasets, we ensure a more rigorous evaluation of the proposed method, making our study comparable to related work that employs these datasets. Moreover, we also selected TDD-PCB due to its generalizability and usage in our specific case: PCB boards share common characteristics that are also relevant to Arduino boards, where we illustrate in Fig. 15(b), which depicts one sample image of the backside of an Arduino board with synthesized defects.

Similarly, we generated CAM heatmaps on sample test images from two PCB defect datasets, as shown in Fig. 12. CGTA-YOLO-F consistently demonstrates stronger attention to small defects across all samples. Moreover, for instance, in Fig. 12(h), YOLOv8 misses multiple mouse bite defects, while our method detects all with higher focus than both YOLOv8 and YOLOv10. Similarly, in Fig. 12(c), YOLOv10 fails to detect an open circuit that YOLOv8 identifies, yet CGTA-YOLO-F not only detects all defects but does so with sharper attention.

### 4.3.3. Model performance comparison: Misalignment classification

As depicted in Fig. 5(b), after detecting components, each predicted bounding box is forwarded to a misalignment classifier, which leverages the model's backbone with switched pre-trained weights rather than a separate classifier. Similarly, the training set is entirely synthesized, while more than half of the test set comprises real components with varying styles, validating the effectiveness and generalizability of our model not only for detection tasks but also for classification, as shown earlier in Fig. 4.

Both convolutional and transformer-based backbones have seen significant progress in recent years, with CNN models like MobileNetv3 [81] excelling in efficient classification tasks, while transformer-based models such as Vision Transformer (ViT) [82] and SwinTransformer [59] have pushed accuracy boundaries, although often with increased computational demands.

As shown in Table 9, CNN backbones like ConvNeXt and Efficient-Net [85,86] achieve relatively high accuracy (91.3%), but come with more parameters and slower speeds. In contrast, lightweight CNNs like MobileNetv3-s offer a better balance of accuracy (90.6%) and speed, making them a practical choice for real-time tasks, where processing speed is important given the large number of components to detect on a typical Arduino board.

Transformer-based models, for example the base ViT model (ViT-B/16) achieves the highest accuracy (91.5%) but require 85.8M parameters and 17.6 GFLOPs, resulting in the slowest speed (3.2 ms/img).
<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /><figcaption>**Fig. 12.** Predictions and CAM heatmaps on sample test data, comparing models. (a)–(b) DeepPCB dataset; (c)–(h) TDD-PCB dataset with defect types: (c) Open, (d) Spur, (e) Spurious, (f) Short, (g) Missing hole, and (h) Mouse bite.</figcaption></figure>

<table><caption>**Table 9**
Accuracy comparison of models for the misalignment classification task.</caption><thead><tr><th>Model</th><th>Acc.</th><th>#Param.</th><th>GFLOPs</th><th>ms/img</th></tr></thead><tbody><tr><td>ResNet50 [83]</td><td>87.6%</td><td>23.5M</td><td>4.1</td><td>2.0</td></tr><tr><td>ShuffleNetv2 [84]</td><td>89.3%</td><td><strong>1.3M</strong></td><td><strong>0.1</strong></td><td>1.1</td></tr><tr><td>MobileNetv3-s [81]</td><td>90.6%</td><td>1.5M</td><td><strong>0.1</strong></td><td>1.2</td></tr><tr><td>EfficientNetv2-s [85]</td><td>91.3%</td><td>20.2M</td><td>8.4</td><td>1.6</td></tr><tr><td>ConvNeXt-t [86]</td><td>91.3%</td><td>27.8M</td><td>4.5</td><td>1.9</td></tr><tr><td>FasterNet-t [64]</td><td>86.5%</td><td>3.9M</td><td>0.3</td><td>1.0</td></tr><tr><td>VIT-B/16 [82]</td><td><strong>91.5%</strong></td><td>85.8M</td><td>17.6</td><td>3.2</td></tr><tr><td>SwinTransformer-t [59]</td><td>90.9%</td><td>27.5M</td><td>4.5</td><td>2.2</td></tr><tr><td>EfficientFormer-l1 [87]</td><td>88.7%</td><td>11.3M</td><td>1.3</td><td>1.3</td></tr><tr><td>MobileViT-s [88]</td><td>90.3%</td><td>4.9M</td><td>1.1</td><td>1.5</td></tr><tr><td>PoolFormer-s12 [89]</td><td>88.9%</td><td>11.4M</td><td>1.8</td><td>1.3</td></tr><tr><td>YOLOv8-cls [20]</td><td>84.1%</td><td>1.4M</td><td>3.4</td><td><strong>0.8</strong></td></tr><tr><td>CGTA-YOLO-F Backbone (Ours)</td><td><strong>91.4%</strong></td><td>1.6M</td><td>3.9</td><td><strong>0.8</strong></td></tr></tbody></table>

Although newer transformer models originally designed for mobile applications such as EfficientFormer and MobileViT [87,88] improve efficiency, they still have higher memory impact compared to the aforementioned lightweight CNNs, limiting their overall practicality.

In contrast, our proposed CGTA-YOLO-F model achieves 91.4% accuracy, comparable to the best transformer models, but with significantly fewer parameters (1.6M) and a faster processing speed (0.8 ms/img). YOLOv8, designed for speed, shows limited generalization when trained on synthesized data and tested on real-world data. Notably, the proposed method outperforms YOLOv8-cls by 7.3% in accuracy, while achieving the fastest speed.

## 4.4. Ablation study

We conducted ablation experiments on the full CGTA-FCGA-F model to assess the impact of each block on the Arduino dataset, which is the key challenge in this research and a more appropriate dataset for studying generalization capabilities with synthetic training. Additionally, a second ablation study focused on the C2f-FCGA block to evaluate the contributions of its sub-components. For clarity, we define the following model variants: CG-YOLO (YOLOv8 + CGB), CGTA-YOLO (YOLOv8 + CGB + TA), and CGTA-YOLO-F (YOLOv8 + CGB + TA + C2f-FCGA).

<table><caption>**Table 10**
Ablation experiment: CGTA-YOLO-F.</caption><thead><tr><th>Model</th><th>mAP<sub>50</sub></th><th>#Param.</th><th>GFLOPs</th><th>ms/img</th></tr></thead><tbody><tr><td>YOLOv8 Baseline</td><td>94.4%</td><td>3.01M</td><td>8.1</td><td>12.1</td></tr><tr><td>CGTA-YOLO</td><td>95.8%</td><td>3.62M</td><td>9.2</td><td>15.0</td></tr><tr><td>CGTA-YOLO-F</td><td><strong>97.4%</strong></td><td>3.65M</td><td>9.4</td><td>18.9</td></tr><tr><td>CGTA-YOLO-F</td><td><strong>97.4%</strong></td><td>3.59M</td><td>9.3</td><td>14.8</td></tr></tbody></table>

### 4.4.1. Ablation experiments on CGTA-FCGA-F

We collected statistics on the test set to study the effect of each introduced block, as shown in Table 10. For model explainability, we tested the models on two real Arduino board images (Fig. 13(a)(b)) to evaluate attention on detected components. Additionally, a synthesized test board (Fig. 13(c)) was used to observe model performance on data sharing the same distribution as the training set, providing insights into generalization ability. On real data, YOLOv8 missed several components and showed lower attention on the detected ones, achieving 94.4% mAP. Adding CGB improved the detection of several missed ones, increasing mAP to 95.8%. Integrating TA in the backbone further enhanced attention and focus on components, boosting mAP to 96.5%. Finally, introducing C2f-FCGA blocks in the neck improved feature fusion, enabling the model to detect tiny components, such as the 16 MHz crystal (missed by previous models) in the first image, and significantly improving feature fusion to guide the model’s attention to the central regions of components, as shown in Fig. 13. Each component uniquely contributes to the model’s performance: CG reduces false negatives, TA enhances focus at the feature extraction level, and C2f-FCGA improves feature fusion. Together, they create a high-performing model, namely CGTA-YOLO-F.

Notably, the attention mechanism in the backbone can enhance the model’s focus on detections, as demonstrated by the comparison between CG-YOLO and CGTA-YOLO. The novel C2f-FCGA block further intensifies focus on the central regions of detected components, especially the small ones such as resistors, capacitors, LEDs, etc. with more structured attention, visually confirming the improved quality of bounding boxes and aligning with the higher mAP reported. This resulted in a final 97.4% mAP, representing a gain of 3% compared to the baseline. While the baseline model performs well on synthetic data (Fig. 13(c)), our model still demonstrated more centralized attention.

The C2f-FCGA block was specifically designed to optimize feature fusion in the neck, where contextual focus and attention mechanisms
<figure><img src="image_17.png" /><figcaption>Fig. 13. Ablation study using heatmaps generated from EigenCAM. The addition of CG reduces false negatives, TA enhances focus on detected components, and the inclusion of C2f-FCGA further centralizes attention, significantly improving detection performance compared to the baseline.</figcaption></figure>

are most effective. While we can theoretically use them to replace the C2f blocks in the backbone, our empirical results revealed a performance trade-off, with a degradation of mAP to 95.2%. Although this is higher than the baseline, the performance decay outweighed the advantages of other architectural improvements. As shown in Fig. 6, the backbone's C2f outputs are concatenated in the neck for feature fusion. Premature replacement with C2f-FCGA blocks risks inducing over-attention that suppresses feature diversity, resulting in feature collapse. This disrupts the backbone's hierarchical structure, which progressively captures low-level to high-level features, and disturbs the balance between spatial and semantic feature extraction due to over-attention in the early stages.

We tested a design choice that retains C2f in the first backbone layers and we replace only the last two C2f blocks with C2f-FCGA in the deeper stages. This approach improved the mAP to 96.5%, comparable to CGTA-YOLO. However, the highest performance was achieved when C2f-FCGA was applied exclusively in the neck, which validates the initial design choice.

The backbone inherently benefits more from global attention mechanisms [22], which are instrumental in preserving feature diversity in the early layers and constructing global dependencies to highlight essential features through the receptive field. In our case, TA proved effective when coupled with CGB, as discussed in Section 4.4.3. Moreover, early backbone layers (e.g., P1, P2, and P3) primarily process features locally and benefit from lightweight operations. Replacing these with C2f-FCGA blocks also introduces computational overhead without proportional gains.

Conversely, the neck, tasked with fusing feature maps from the backbone, benefits significantly from C2f-FCGA's localized attention, which enhances feature discrimination and prioritizes key areas in the fused maps. This distinction underscores the importance of assigning attention mechanisms to stages where they are most effective.

### 4.4.2. Ablation experiments: FCGA bottleneck

We evaluated the impact of the proposed C2f-FCGA block by isolating the FCGA bottleneck to compare its performance with other configurations in the neck, as shown in Table 11. Replacing C2f with the FasterNet bottleneck for reduced parameters and FLOPs led to a slight drop in mAP from 94.4% to 93.8%, showing a trade-off between reduced complexity and performance. Replacing partial convolution with CGB enhanced mAP to 95.2%, indicating improved feature fusion with minimal parameter increase.

Using the whole TA architecture resulted in a modest drop in mAP to 94.9%, indicating that the complex interaction of spatial, channel, and height-width attention may disturb feature refinement at

<table><caption>Table 11
Ablation experiment: FCGA bottleneck.</caption><thead><tr><td>Model</td><td>mAP<sub>50</sub></td><td>#Param.</td><td>GFLOPs</td></tr></thead><tbody><tr><td>Original bottleneck</td><td>94.4%</td><td>3.01M</td><td>8.1</td></tr><tr><td>FasterNet</td><td>93.7%</td><td>2.67M</td><td>7.5</td></tr><tr><td>FasterCG</td><td>95.2%</td><td>2.95M</td><td>8.1</td></tr><tr><td>FasterCGTA</td><td>94.9%</td><td>2.95M</td><td>8.2</td></tr><tr><td>FasterCGsA (FCGA)</td><td>95.8%</td><td>2.95M</td><td>8.2</td></tr></tbody></table>

this point. Our FCGA bottleneck presents a simplified attention that keeps only the channel-width attention. This addresses the issue by streamlining the focus method, raising mAP to 95.8% without additional computational complexity. These findings shows the necessity of a targeted and simplified attention mechanism in the neck to improve feature fusion without over-complicating, resulting in greater performance when combined with CG and TA in our suggested model.

### 4.4.3. Comparative study of attention mechanisms coupled with CGB

The CGTA-YOLO-F model proposed in this paper adopts the design choice of coupling CGB with TA in the backbone to enhance feature extraction. This section evaluates the impact of various recent and popular attention mechanisms, coupled with CGB in the model, i.e. CG-YOLO, using the main Arduino dataset by comparing their performance and efficiency using metrics such as mAP, total parameters, parameters of the attention mechanism alone, and GFLOPs.

As shown in Table 12, TA achieves a remarkable mAP of 96.5%, outperforming convolution-based methods like CBAM [74] (95.0%), SE [73] (92.7%), and even the multi-scale convolutional attention (MSCA) module [90] (96.0%) while introducing significantly fewer parameters (200 vs. 8563, 512, and 93,952, respectively). Efficient Multi-Scale Attention (EMA) [91] demonstrates a low parameter count and a relatively high mAP (96.1%), although still below the combined usage of TA and CGB. An alternate lightweight block that achieves good results by balancing efficiency and performance is Mixed Local Channel Attention (MLCA) [92], which uses significantly lower parameters while achieving the same performance as PSA.

Compared to transformer-based methods, TA's efficiency is even more pronounced. While Swin Transformer [93] achieves the highest mAP (96.9%), it requires over 1650x more attention parameters (331,268 vs. 200) and doubles the GFLOPs (19.6 vs. 9.4), making it computationally prohibitive for high-throughput tasks like misalignment detection, which involves processing up to 42 cropped images per sample. Similarly, PSA [22], a lightweight self-attention [9]-inspired method, achieves slightly lower mAP (96.2%) than TA, reinforcing
<figure><img src="image_18.png" /><figcaption>**Fig. 14.** Inference speed comparison on different hardware with different hardware acceleration strategies. Note: the speed of the M2 SoC is tested on an iPad, yielding lower performance after acceleration compared to the M1 SoC on a laptop. A14 SoC is installed on iPhone 12 series.</figcaption></figure>

<table><caption>**Table 12**
Experiments on effects of different attention mechanisms when coupled with CGB.</caption><thead><tr><th>Attention</th><th>mAP<sub>50</sub></th><th>#Param.<br>(Attention)</th><th>#Param.<br>(Total)</th><th>GFLOPs</th></tr></thead><tbody><tr><td>TA [34]</td><td>96.5%</td><td>200</td><td>3.7M</td><td>9.4</td></tr><tr><td>SE [73]</td><td>92.7%</td><td>512</td><td>3.7M</td><td>9.4</td></tr><tr><td>CBAM [74]</td><td>95.0%</td><td>8563</td><td>3.7M</td><td>9.4</td></tr><tr><td>MLCA [92]</td><td>96.2%</td><td>10</td><td>3.7M</td><td>9.4</td></tr><tr><td>EMA [91]</td><td>96.1%</td><td>14</td><td>3.7M</td><td>9.4</td></tr><tr><td>Shuffle [94]</td><td>94.3%</td><td>96</td><td>3.7M</td><td>9.4</td></tr><tr><td>MSCA [90]</td><td>96.0%</td><td>93,952</td><td>3.8M</td><td>9.4</td></tr><tr><td>MobileVIT [95]</td><td>93.4%</td><td>197,633</td><td>3.9M</td><td>9.5</td></tr><tr><td>SwinTransformer [93]</td><td>96.9%</td><td>331,268</td><td>4.0M</td><td>19.6</td></tr><tr><td>PSA [22]</td><td>96.2%</td><td>249,728</td><td>3.9M</td><td>9.6</td></tr></tbody></table>

TA's superior trade-off between accuracy and efficiency. Moreover, TA's modularity allows it to partially integrate into the proposed FCGA bottleneck, enabling faster installation for simplified attention for better integration at hardware deployment runtime. Hence, coupling CG with TA achieves a superior balance of accuracy and resource usage, making it the most viable option for scalable deployment.

## 4.5. Hardware acceleration and model deployment

Hardware acceleration means improving model inference speed on specific hardware by optimizing the computational graph. We use industry-standard tools, such as TensorRT for NVIDIA GPUs, OpenVINO for Intel CPUs, ONNX for ARM-Compliant CPUs and also CoreML for Apple System on Chips (SoCs), leveraging their unique unified memory architecture for heterogeneous computing. We convert the model to FP16 precision which is a common standard in deployment and also a fixed requirement for Apple's Neural Engine.

Therefore, we tested our model on different hardware to see how well it performed, as shown in Fig. 14. On the high-end NVIDIA A100 GPU with TensorRT acceleration, our model processes at 2.9 ms per image (345 FPS), 3.8x faster than the raw PyTorch version, showing it is very effective in real-time situations. After optimization, the misalignment classifier runs at 0.6 ms/img.

To help smaller third-party factories that usually have fewer resources and might not have access to high-quality GPUs like the A100, we also tested our model on a basic GTX 1060 GPU. Even with these limitations, using TensorRT acceleration, the model achieved 19.1 ms/img (52.4 FPS) for component detection, showing that real-time performance probably holds even on less powerful hardware.

We also tested on edge processors like the M1 SoC in Mac and A14 SoC in the iPhone 12. Deployed via CoreML tool with heterogeneous

computing (ARMv8 Firestorm CPU + NPU), the model operates at 27.6 ms/img (36 FPS), showing flexibility across platforms. On the M1 SoC, the faster model processes at 21.1 ms/img, which is 10.7x quicker faster PyTorch's 224.9 ms/img.

Assuming no missing component for a perfect Arduino board with up to 42 component per image, the total processing time using parallel threading for component and missing component detection and misalignment classification is 28 ms/img (36 FPS) on A100. The CoreML deployment speeds up the misalignment classifier to 0.56 ms/img, leading to a total processing time of 44.6 ms/img (22.4 FPS) for full checks (component + alignment). These results show our method's real-time effectiveness and versatility across different hardware setups.

In industrial settings, hardware choice should balance performance, cost and scalability to meet different operational needs. High-quality GPUs like the A100 perform very well in centralized and high-speed production lines, which is able to provide computing power across multiple factories at the same time. Smaller factories with limited budgets might benefit from cheaper options like basic GPUs or even ARM processors, which offer compact, energy-efficient solutions and can even be directly built into small and embedded inspection devices combined with optical cameras. These systems lower latency and complexity, remove the need for centralized cloud computing and are perfect for distributed IoT-enabled factories.

Additionally, while power consumption may not be a primary concern for large factories, the operational costs of scaling GPUs, along with challenges such as integration complexity, heat management, and hardware availability, can become cumbersome in smaller-scale or distributed deployments. Edge devices that process data locally offer a practical solution for factories with limited resources and still provide reliable performance. More importantly, local inference ensures data privacy eliminating the needs to communicate with a central server.

The demonstrated real-time performance of our CGTA-YOLO-F model across these hardware platforms ensures consistent quality assurance, minimizes downtime and reduces overall operational costs.

## 4.6. Graphical User Interface (GUI)

An industrial software application has been created, as shown in Fig. 15. The software detects the user's environment and uses hardware-accelerated models for inference with an auto-check of the user's hardware and software environment. It supports all operating systems with any CPU, NVIDIA GPUs, and Apple's ARM-compliant CPUs with NPU. A sample image is chosen to display the predicted results containing correctly placed components, missing components, and misaligned ones with their confidence score printed next to their bounding boxes.
<figure><img src="image_19.png" /><figcaption>Fig. 15. GUI of the Arduino Uno Quality Assurance and Defect Detection software. (a) Front side — components; (b) Back side — PCB defects.</figcaption></figure>

The user can easily adjust the brightness level of the image under test, and set the confidence threshold by adjusting the sliders, and activate misalignment classification by clicking the corresponding checkbox. The front-side component detection and back-side defect detection can also be switched. By clicking on the detect button, it initiates the AI inference and will display detailed results in the GUI and write relevant information in the terminal.

## 5. Conclusion

Given the open-source nature of Arduino, a large number of replicas have been produced, introducing quality variability that makes it challenging to compile extensive datasets because of the variety of styles and defects. In this work, learning from synthesized data for quality assurance turns out to be a viable method.

We developed CGTA-YOLO-F, a lightweight defect detection and quality assurance model that streamlines object detection and classification within a unified framework with optimized runtime. The model integrates context-guided blocks and triplet attention for better feature extraction, highlighting the interaction between local and global features. It also introduces the novel C2f-FCGA block, which efficiently captures multi-scale spatial dependencies to improve feature fusion. The proposed method demonstrates superior generalizability when trained entirely on synthesized data and evaluated on real test samples, effectively addressing the challenges posed by style variability in defect detection. It achieves notable improvements in both detection and classification metrics when evaluated on the Arduino dataset and two other PCB defect datasets, as well as on the misalignment classification task with its reconfigurable backbone.

Optimized for different types of hardware, CGTA-YOLO-F can be deployed on both high-end GPUs for cloud computing and smaller processors for edge inference. Although using synthesized data might limit the model's ability to capture all real-world variations, future work could look at growing data collections by collaborating with manufacturers and improving the model to cover more types of defects. Generally, the model provides a strong and flexible tool for learning from synthesized data for quality assurance and defect detection in microcontroller manufacturing, offering a solution to meet the needs of many factory settings.

## CRediT authorship contribution statement

**Zhifan Song:** Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis. **Abd Al Rahman M. Abu Ebayyeh:** Writing – review & editing, Validation, Supervision, Resources, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgment

This research has received no external funding.

## Data availability

Data will be made available on request.

## References

[1] A.A.R.M. Abu Ebayyeh, A. Mousavi, A review and analysis of automatic optical inspection and quality monitoring methods in electronics industry, IEEE Access 8 (2020) 183192–183271, http://dx.doi.org/10.1109/ACCESS.2020.3029127.

[2] W. Xie, D. Zheng, Z. Li, Y. Wang, L. Wang, Digital technology and manufacturing industrial change: Evidence from the Chinese manufacturing industry, Comput. Ind. Eng. 187 (2024) 109825, http://dx.doi.org/10.1016/j.cie.2023.109825.

[3] X. Chen, Y. Wu, X. He, W. Ming, A comprehensive review of deep learning-based PCB defect detection, IEEE Access 11 (2023) 139017–139038, http://dx.doi.org/10.1109/ACCESS.2023.3339561.

[4] R. Ding, L. Dai, G. Li, H. Liu, TDD-Net: A tiny defect detection network for printed circuit boards, CAAI Trans. Intell. Technol. 4 (2019) http://dx.doi.org/10.1049/trit.2019.0019.

[5] R. Seidel, B. Rachinger, N. Thielen, K. Schmidt, S. Meier, J. Franke, Development and validation of a digital twin framework for smt manufacturing, Comput. Ind. 145 (2023) 103831, http://dx.doi.org/10.1016/j.compind.2022.103831.

[6] Y.-S. Deng, A.-C. Luo, M.-J. Dai, Building an automatic defect verification system using deep neural network for PCB defect classification, in: 2018 4th International Conference on Frontiers of Signal Processing, ICFSP, 2018, pp. 145–149, http://dx.doi.org/10.1109/ICFSP.2018.8552045.

[7] J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: Unified, real-time object detection, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, IEEE Computer Society, Los Alamitos, CA, USA, 2016, pp. 779–788, http://dx.doi.org/10.1109/CVPR.2016.91.

[8] S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: Towards real-time object detection with region proposal networks, in: C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems, Vol. 28, Curran Associates, Inc., 2015.

[9] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L.u. Kaiser, I. Polosukhin, Attention is all you need, in: I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett (Eds.), Advances in Neural Information Processing Systems, Vol. 30, Curran Associates, Inc., 2017.

[10] N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, S. Zagoruyko, End-to-end object detection with transformers, in: A. Vedaldi, H. Bischof, T. Brox, J.-M. Frahm (Eds.), Computer Vision – ECCV 2020, Springer International Publishing, Cham, 2020, pp. 213–229.

[11] Y. Zhao, W. Lv, S. Xu, J. Wei, G. Wang, Q. Dang, Y. Liu, J. Chen, DETRs beat YOLOs on real-time object detection, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2024, pp. 16965–16974.
[12] M. Yuan, Y. Zhou, X. Ren, H. Zhi, J. Zhang, H. Chen, YOLO-HMC: An improved method for PCB surface defect detection, IEEE Trans. Instrum. Meas. 73 (2024) 1–11.

[13] M. Lu, W. Sheng, Y. Zou, Y. Chen, Z. Chen, WSS-YOLO: An improved industrial defect detection network for steel surface defects, Measurement 236 (2024) 115060, http://dx.doi.org/10.1016/j.measurement.2024.115060.

[14] B. Huang, Y. Ding, G. Liu, G. Tian, S. Wang, ASD-YOLO: An aircraft surface defects detection method using deformable convolution and attention mechanism, Measurement 238 (2024) 115300, http://dx.doi.org/10.1016/j.measurement.2024.115300.

[15] S.B. Jha, R.F. Babiceanu, Deep CNN-based visual defect detection: Survey of current literature, Comput. Ind. 148 (2023) 103911, http://dx.doi.org/10.1016/j.compind.2023.103911.

[16] L. Gao, J. Zhang, C. Yang, Y. Zhou, Cas-VSwin transformer: A variant swin transformer for surface-defect detection, Comput. Ind. 140 (2022) 103689, http://dx.doi.org/10.1016/j.compind.2022.103689.

[17] J. Yao, J. Li, AYOLOv3-Tiny: An improved convolutional neural network architecture for real-time defect detection of PAD light guide plates, Comput. Ind. 136 (2022) 103588, http://dx.doi.org/10.1016/j.compind.2021.103588.

[18] T. Niu, Z. Xie, J. Zhang, L. Tang, B. Li, H. Wang, A generalized well neural network for surface defect segmentation in optical communication devices via template-testing comparison, Comput. Ind. 151 (2023) 103978, http://dx.doi.org/10.1016/j.compind.2023.103978.

[19] M. Hussain, YOLO-v1 to YOLO-v8, and the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection, Machines 11 (7) (2023) http://dx.doi.org/10.3390/machines11070677, URL https://www.mdpi.com/2075-1702/11/7/677.

[20] G. Jocher, A. Chaurasia, J. Qiu, Ultralytics YOLO, 2023, URL https://github.com/ultralytics/ultralytics.

[21] C.-Y. Wang, I.-H. Yeh, H.-Y.M. Liao, YOLOv9: Learning what you want to learn using programmable gradient information, 2024, arXiv:2402.13616. URL https://arxiv.org/abs/2402.13616.

[22] A. Wang, H. Chen, L. Liu, K. Chen, Z. Lin, J. Han, G. Ding, YOLOv10: Real-time end-to-end object detection, 2024, arXiv:2405.14458.

[23] V. Breive, T. Sledevic, Person detection in thermal images: A comparative analysis of YOLOv8 and YOLOv9 models, in: 2024 IEEE Open Conference of Electrical, Electronic and Information Sciences (EStream), 2024, pp. 1–4, http://dx.doi.org/10.1109/eStream61684.2024.10542600.

[24] J. Tang, S. Liu, D. Zhao, L. Tang, W. Zou, B. Zheng, PCB-YOLO: An improved detection algorithm of PCB surface defects based on YOLOv5, Sustainability 15 (7) (2023) 5963.

[25] Q. Ling, N.A.M. Isa, M.S.M. Asaari, Precise detection for dense PCB components based on modified YOLOv8, IEEE Access 11 (2023) 116545–116560.

[26] B.C. Bothma, N. Luwes, Exploring the use of synthetic training data for the classification of electronic components in artificial intelligence systems, in: 2024 16th International Conference on Human System Interaction, HSI, 2024, pp. 1–6, http://dx.doi.org/10.1109/HSI61632.2024.10613537.

[27] L.K. Cheong, S.A. Suandi, S. Rahman, Defects and components recognition in printed circuit boards using convolutional neural network, in: M.A.M. Zawawi, S.S. Teoh, N.B. Abdullah, M.I.S. Mohd Sazali (Eds.), 10th International Conference on Robotics, Vision, Signal Processing and Power Applications, Springer Singapore, Singapore, 2019, pp. 75–81.

[28] J.-D. Song, Y.-G. Kim, T.-H. Park, SMT defect classification by feature extraction region optimization and machine learning, Int. J. Adv. Manuf. Technol. 101 (2019) 1–11, http://dx.doi.org/10.1007/s00170-018-3022-6.

[29] C. Wu, D. Wang, A. Ip, D. Wang, C. Chan, H. Wang, A particle swarm optimization approach for components placement inspection on printed circuit boards, J. Intell. Manuf. 20 (2008) 551.

[30] F.A. Ruis, A.M. Liezenga, F.G. Heslinga, L. Ballan, T.A. Eker, R.J.M. den Hollander, M.C. van Leeuwen, J. Dijk, W. Huizinga, Improving object detector training on synthetic data by starting with a strong baseline methodology, 2024, arXiv:2405.19822. URL https://arxiv.org/abs/2405.19822.

[31] D. Schraml, G. Notni, Synthetic training data in AI-driven quality inspection: The significance of camera, lighting, and noise parameters, Sensors 24 (2) (2024) URL https://www.mdpi.com/1424-8220/24/2/649.

[32] J. Li, J. Gu, Z. Huang, J. Wen, Application research of improved YOLO V3 algorithm in PCB electronic component detection, Appl. Sci. 9 (18) (2019) http://dx.doi.org/10.3390/app9183750.

[33] T. Wu, S. Tang, R. Zhang, J. Cao, Y. Zhang, CGNet: A light-weight context guided network for semantic segmentation, IEEE Trans. Image Process. 30 (2021) 1169–1179.

[34] D. Misra, T. Nalamada, A.U. Arasanipalai, Q. Hou, Rotate to attend: Convolutional triplet attention module, in: 2021 IEEE Winter Conference on Applications of Computer Vision, WACV, 2021, pp. 3138–3147, http://dx.doi.org/10.1109/WACV48630.2021.00318.

[35] A.J. Crispin, V. Rankov, Automated inspection of PCB components using a genetic algorithm template-matching approach, Int. J. Adv. Manuf. Technol. 35 (3–4) (2007) 293–300.

[36] K. Zhang, Using deep learning to automatic inspection system of printed circuit board in manufacturing industry under the internet of things, Comput. Sci. Inf. Syst. 20 (2) (2023) 723–741.

[37] J. Shen, N. Liu, H. Sun, Defect detection of printed circuit board based on lightweight deep convolution network, IET Image Process. 14 (15) (2020) 3932–3940.

[38] S. Ghosh, M.T. Mostafiz, S.R. Gurudu, S. Taheri, N. Asadizanjani, PCB component detection for hardware assurance: A feature selection-based approach, in: 2022 IEEE International Symposium on Hardware Oriented Security and Trust, HOST, 2022, pp. 109–112, http://dx.doi.org/10.1109/HOST54066.2022.9840298.

[39] S. Cao, I. Parviziomran, H. Yang, S. Park, D. Won, Prediction of component shifts in pick and place process of surface mount technology using support vector regression, Procedia Manuf. 39 (2019) 210–217, http://dx.doi.org/10.1016/j.promfg.2020.01.316, 25th International Conference on Production Research Manufacturing Innovation: Cyber Physical Manufacturing August 9-14, 2019 | Chicago, Illinois (USA).

[40] S. Mashohor, J.R. Evans, A.T. Erdogan, Automatic hybrid genetic algorithm based printed circuit board inspection, in: Proceedings of the 1st NASA/ESA Conference on Adaptive Hardware and Systems, AHS, 2006, pp. 390–400.

[41] W. Zhao, S. Gurudu, S. Taheri, S. Ghosh, M.M. Sathiaseelan, N. Asadizanjani, PCB component detection using computer vision for hardware assurance, Big Data Cogn. Comput. 6 (2022) 39.

[42] C.-Y. Wang, A. Bochkovskiy, H.-Y.M. Liao, YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2023.

[43] Z. Li, A. Li, Y. Zhang, X. Kong, W. Li, BEW-YOLO: An improved method for PCB defect detection based on YOLOv7, in: 2023 IEEE 29th International Conference on Parallel and Distributed Systems, ICPADS, 2023, pp. 268–274.

[44] G. Xiao, S. Hou, H. Zhou, PCB defect detection algorithm based on CDI-YOLO, Sci. Rep. 14 (2024) 7351.

[45] H. Lan, H. Zhu, R. Luo, Q. Ren, C. Chen, PCB defect detection algorithm of improved YOLOv8, in: 2023 8th International Conference on Image, Vision and Computing, ICIVC, 2023, pp. 178–183.

[46] G. Jocher, YOLOv5 by ultralytics, in: Github Repository, 2020, http://dx.doi.org/10.5281/zenodo.3908559, URL https://github.com/ultralytics/yolov5.

[47] H. Xu, FCD-YOLO: Improved YOLOv5 based on decoupled head and attention mechanism for defect detection on printed circuit board, in: 2022 2nd International Conference on Networking Systems of AI, INSAI, 2022, pp. 7–11.

[48] J. Wang, K. Chen, R. Xu, Z. Liu, C.C. Loy, D. Lin, CARAFE: Content-aware Re-Assembly of features, in: 2019 IEEE/CVF International Conference on Computer Vision, ICCV, 2019, pp. 3007–3016.

[49] W. Chen, Z. Huang, Q. Mu, Y. Sun, PCB defect detection method based on transformer-YOLO, IEEE Access 10 (2022) 129480–129489.

[50] Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo, Swin transformer: Hierarchical vision transformer using shifted windows, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, ICCV, 2021.

[51] G. Shao, Y. Zheng, T. Li, J. Wu, J. Luo, F. Gao, J. Ma, T. Liu, An improved YOLOv3 network for PCB defect detection, in: 2021 China Automation Congress, CAC, 2021, pp. 1819–1823, http://dx.doi.org/10.1109/CAC53003.2021.9728216.

[52] J.S.S.V. Mamidi, S. Sameer, J. Bayana, A light weight version of PCB defect detection system using YOLO V4 tiny, in: 2022 International Mobile and Embedded Technology Conference, MECON, 2022, pp. 441–445, http://dx.doi.org/10.1109/MECON53876.2022.9752361.

[53] H. Xin, Z. Chen, B. Wang, PCB electronic component defect detection method based on improved YOLOv4 algorithm, J. Phys.: Conf. Ser. 1827 (1) (2021) 012167, http://dx.doi.org/10.1088/1742-6596/1827/1/012167.

[54] J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, P. Abbeel, Domain randomization for transferring deep neural networks from simulation to the real world, in: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 2017, pp. 23–30, http://dx.doi.org/10.1109/IROS.2017.8202133.

[55] C. Feng, Y. Zhong, Z. Jie, W. Xie, L. Ma, InstaGen: Enhancing object detection by training on synthetic dataset, in: Proceedings of the IEEE / CVF Computer Vision and Pattern Recognition, 2024.

[56] B. Vanherle, S. Moonen, F.V. Reeth, N. Michiels, Analysis of training object detection models with synthetic data, in: 33rd British Machine Vision Conference 2022, BMVC 2022, London, UK, November 21-24, 2022, BMVA Press, 2022, URL https://bmvc2022.mpi-inf.mpg.de/0833.pdf.

[57] O. Albrecht, R.D.J. Höhne, D. Barkur, K. Meier, K. Bock, Study on using noisy synthetic data for neural networks to assess thermo-mechanical reliability parameters of solder interconnects, in: 2023 IEEE 25th Electronics Packaging Technology Conference, EPTC, 2023, pp. 751–756, http://dx.doi.org/10.1109/EPTC59621.2023.10457730.

[58] Y. Liu, H. Wu, Automatic solder defect detection in electronic components using transformer architecture, IEEE Trans. Compon. Packag. Manuf. Technol. 14 (1) (2024) 166–175, http://dx.doi.org/10.1109/TCPMT.2023.3344096.

[59] Z. Liu, H. Hu, Y. Lin, Z. Yao, Z. Xie, Y. Wei, J. Ning, Y. Cao, Z. Zhang, L. Dong, F. Wei, B. Guo, Swin transformer V2: Scaling up capacity and resolution, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2022, pp. 12009–12019.

[60] K. He, G. Gkioxari, P. Dollár, R. Girshick, Mask R-CNN, in: 2017 IEEE International Conference on Computer Vision, ICCV, 2017, pp. 2980–2988, http://dx.doi.org/10.1109/ICCV.2017.322.
[61] H.D. Nguyen, D. Cheng, X. Wang, Y. Shi, B. Wen, Data-efficient deep learning for printed circuit board defect detection using X-Ray images, IEEE Trans. Instrum. Meas. (2025) http://dx.doi.org/10.1109/TIM.2025.3529089, 1-1.

[62] S. Tang, F. He, X. Huang, J. Yang, Online PCB defect detector on a new PCB defect dataset, 2019, arXiv preprint arXiv:1902.06197.

[63] A. Whitham, Arduino uno R3 3D model, 2014, Available at: https://grabcad.com/library/Arduino-uno-r3-1.

[64] J. Chen, S.-h. Kao, H. He, W. Zhuo, S. Wen, C.-H. Lee, S.-H.G. Chan, Run, don't walk: Chasing higher FLOPS for faster neural networks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2023, pp. 12021-12031.

[65] C.-Y. Wang, H.-Y. Mark Liao, Y.-H. Wu, P.-Y. Chen, J.-W. Hsieh, I.-H. Yeh, CSPNet: A new backbone that can enhance learning capability of CNN, in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW, 2020, pp. 1571-1580, http://dx.doi.org/10.1109/CVPRW50498.2020.00203.

[66] C. Wang, A. Bochkovskiy, H. Liao, YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, in: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, IEEE Computer Society, Los Alamitos, CA, USA, 2023, pp. 7464-7475, http://dx.doi.org/10.1109/CVPR52729.2023.00721.

[67] K. He, X. Zhang, S. Ren, J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Trans. Pattern Anal. Mach. Intell. 37 (9) (2015) 1904-1916, http://dx.doi.org/10.1109/TPAMI.2015.2389824.

[68] T. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, S. Belongie, Feature pyramid networks for object detection, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, IEEE Computer Society, Los Alamitos, CA, USA, 2017, pp. 936-944, http://dx.doi.org/10.1109/CVPR.2017.106.

[69] S. Liu, L. Qi, H. Qin, J. Shi, J. Jia, Path aggregation network for instance segmentation, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 8759-8768, http://dx.doi.org/10.1109/CVPR.2018.00913.

[70] X. Li, W. Wang, L. Wu, S. Chen, X. Hu, J. Li, J. Tang, J. Yang, Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection, in: NeurIPS, 2020.

[71] Z. Zheng, P. Wang, D. Ren, W. Liu, R. Ye, Q. Hu, W. Zuo, Enhancing geometric factors in model learning and inference for object detection and instance segmentation, IEEE Trans. Cybern. 52 (8) (2021) 8574-8586, http://dx.doi.org/10.1109/TCYB.2021.3095305.

[72] T. Wu, S. Tang, R. Zhang, J. Cao, Y. Zhang, CGNet: A light-weight context guided network for semantic segmentation, IEEE Trans. Image Process. 30 (2021) 1169-1179, http://dx.doi.org/10.1109/TIP.2020.3042065.

[73] J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132-7141, http://dx.doi.org/10.1109/CVPR.2018.00745.

[74] S. Woo, J. Park, J.-Y. Lee, I.S. Kweon, CBAM: Convolutional block attention module, in: V. Ferrari, M. Hebert, C. Sminchiescu, Y. Weiss (Eds.), Computer Vision - ECCV 2018, Springer International Publishing, Cham, 2018, pp. 3-19.

[75] S. Elfwing, E. Uchibe, K. Doya, Sigmoid-weighted linear units for neural network function approximation in reinforcement learning, Neural Netw. 107 (2018) 3-11, http://dx.doi.org/10.1016/j.neunet.2017.12.012, Special issue on deep reinforcement learning.

[76] I. Loshchilov, F. Hutter, Decoupled weight decay regularization, in: International Conference on Learning Representations, 2019.

[77] A. Bochkovskiy, C. Wang, H.M. Liao, YOLOv4: Optimal speed and accuracy of object detection, 2020, CoRR abs/2004.10934. arXiv:2004.10934. URL https://arxiv.org/abs/2004.10934.

[78] G. Ghiasi, Y. Cui, A. Srinivas, R. Qian, T.-Y. Lin, E.D. Cubuk, Q.V. Le, B. Zoph, Simple copy-paste is a strong data augmentation method for instance segmentation, in: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2021, pp. 2917-2927, http://dx.doi.org/10.1109/CVPR46437.2021.00294.

[79] G. Jocher, J. Qiu, A. Chaurasia, Ultralytics YOLO, 2024, URL https://github.com/ultralytics/ultralytics.

[80] M. Bany Muhammad, M. Yeasin, Eigen-CAM: Visual explanations for deep convolutional neural networks, SN Comput. Sci. 2 (1) (2021).

[81] A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, Q.V. Le, H. Adam, Searching for MobileNetV3, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, ICCV, 2019.

[82] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale, in: International Conference on Learning Representations, 2021.

[83] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2016, pp. 770-778, http://dx.doi.org/10.1109/CVPR.2016.90.

[84] N. Ma, X. Zhang, H.-T. Zheng, J. Sun, ShuffleNet V2: Practical guidelines for efficient CNN architecture design, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018.

[85] M. Tan, Q. Le, EfficientNetV2: Smaller models and faster training, in: M. Meila, T. Zhang (Eds.), Proceedings of the 38th International Conference on Machine Learning, in: Proceedings of Machine Learning Research, vol. 139, PMLR, 2021, pp. 10096-10106.

[86] Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, S. Xie, A ConvNet for the 2020s, Proc. the IEEE/ CVF Conf. Comput. Vis. Pattern Recognit. ( CVPR) (2022).

[87] Y. Li, G. Yuan, Y. Wen, J. Hu, G. Evangelidis, S. Tulyakov, Y. Wang, J. Ren, EfficientFormer: Vision transformers at MobileNet speed, in: S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh (Eds.), Advances in Neural Information Processing Systems, Vol. 35, Curran Associates, Inc., 2022, pp. 12934-12949.

[88] S. Mehta, M. Rastegari, MobileViT: Light-weight, general-purpose, and mobile-friendly vision transformer, in: International Conference on Learning Representations, 2022.

[89] W. Yu, M. Luo, P. Zhou, C. Si, Y. Zhou, X. Wang, J. Feng, S. Yan, Metaformer is actually what you need for vision, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10819-10829.

[90] M.-H. Guo, C.-Z. Lu, Q. Hou, Z. Liu, M.-M. Cheng, S.-m. Hu, SegNeXt: Rethinking convolutional attention design for semantic segmentation, in: S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh (Eds.), Advances in Neural Information Processing Systems, Vol. 35, Curran Associates, Inc., 2022, pp. 1140-1156, URL https://proceedings.neurips.cc/paper_files/paper/2022/file/08050F40ff41616ccfc3080e60a301a-Paper-Conference.pdf.

[91] D. Ouyang, S. He, G. Zhang, M. Luo, H. Guo, J. Zhan, Z. Huang, Efficient multi-scale attention module with cross-spatial learning, in: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, 2023, pp. 1-5, http://dx.doi.org/10.1109/ICASSP49357.2023.10096516.

[92] D. Wan, R. Lu, S. Shen, T. Xu, X. Lang, Z. Ren, Mixed local channel attention for object detection, Eng. Appl. Artif. Intell. 123 (2023) 106442, http://dx.doi.org/10.1016/j.engappai.2023.106442.

[93] Z. Liu, H. Hu, Y. Lin, Z. Yao, Z. Xie, Y. Wei, J. Ning, Y. Cao, Z. Zhang, L. Dong, F. Wei, B. Guo, Swin transformer V2: Scaling up capacity and resolution, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2022, pp. 12009-12019.

[94] Q.-L. Zhang, Y.-B. Yang, SA-Net: Shuffle attention for deep convolutional neural networks, in: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, 2021, pp. 2235-2239, http://dx.doi.org/10.1109/ICASSP39728.2021.9414568.

[95] S. Mehta, M. Rastegari, Separable self-attention for mobile vision transformers, Trans. Mach. Learn. Res. (2023).