# Real-Time Detection of Hole-Type Defects on Industrial Components Using Raspberry Pi 5
## 2 Related Work
[4] demonstrated that integrating continuous time-frequency gradients with SNNs significantly enhances their ability to distinguish subtle and overlapping fault patterns in noisy environments. Similarly, [PERSON] et al. [7] developed a Wavelet Gradient Integrated SNN for bearing fault detection, achieving higher accuracy than conventional CNNs. In the context of compound fault diagnosis, domain adaptation and reinforcement learning techniques are gaining traction. [PERSON] et al. [6] proposed a Domain Reinforcement Feature Adaptation framework that aligns latent representations of single and compound faults, while [PERSON] et al. [5] introduced the RAOS framework, which leverages adversarial reinforcement learning to recognize complex fault combinations using only single-fault training data. Another emerging challenge in real-world deployment is open-set recognition, where unseen defect types may appear during inference. [PERSON] et al. [9] addressed this with a dual-adversarial structure and Selective Momentum Contrast, improving generalization across known and unknown classes. The advancements in lightweight architectures like EfficientNet and MobileNet are pivotal for enhancing defect detection capabilities on resource-constrained devices such as the Raspberry Pi, ensuring efficient performance in real-time applications. [PERSON] et al. [28] optimized deep learning models for deployment on Raspberry Pi by applying pruning, quantization, and TensorFlow Lite conversion techniques. Their evaluation of custom CNNs and pre-trained models like MobileNetV2 and ResNet50 showed that MobileNet-based models offered the best trade-off between accuracy and speed, while Arm NN Delegate achieved the lowest inference latency, making the approach suitable for real-time edge applications. Edge deployment of convolutional neural networks (CNNs) remains a challenging frontier in real-time AI applications. [PERSON] [29] extensively discusses the difficulties encountered when implementing deep learning models such as MobileNet-V2 and ResNet-50 on memory-constrained and low-power devices, particularly in the context of real-time facemask detection. This issue is directly relevant to industrial defect detection systems, where achieving real-time performance on devices like the Raspberry Pi necessitates a delicate balance between accuracy, model size, and inference speed. A major gap identified in [PERSON]'s work is the lack of optimized deployment methodologies tailored for edge environments. While lightweight models show promise, the study reveals ongoing challenges in minimizing disk footprint and computational load without sacrificing classification metrics such as precision, recall, and F1-score. The challenge of deploying deep learning models on constrained edge devices is not unique to industrial settings; it spans critical domains such as healthcare, where the need for real-time, low-latency AI is equally vital. In this context, [PERSON] et al. [30] present a compelling study focused on continuous health monitoring via wearable sensors, where deep CNNs are used to classify Electrocardiogram (ECG) signals in real time. Their work exposes significant barriers posed by the memory and computational demands of traditional Deep Neural Networks (DNNs), which often inhibit practical deployment in time-sensitive applications. To overcome these limitations, [PERSON] et al. [30] propose a lightweight ShuffleNet-based CNN, optimized through innovative techniques such as focal loss, sliding window preprocessing, and variable stride augmentation to address class imbalance. These strategies not only improve classification accuracy but also drastically reduce the model's resource footprint, offering critical lessons for similar challenges in industrial defect detection systems, where imbalanced datasets and noisy inputs are common.