# YOLO-RRL: A Lightweight Algorithm for PCB Surface Defect Detection
###### Abstract  
Printed circuit boards present several challenges to the detection of defects, including targets of insufficient size and distribution, a high level of background noise, and a variety of complex types. These factors contribute to the difficulties encountered by PCB defect detection networks in accurately identifying defects. This paper proposes a less-parametric model, YOLO-RRL, based on the improved YOLOv8 architecture. The YOLO-RRL model incorporates four key improvement modules: The following modules have been incorporated into the proposed model: Robust Feature Downsampling (RFD), Reparameterised Generalised FPN (RepGFPN), Dynamic Upsampler (DySample), and Lightweight Asymmetric Detection Head (LADH-Head). The results of multiple performance metrics evaluation demonstrate that YOLO-RRL enhances the mean accuracy (mAP) by 2.2 percentage points to 95.2%, increases the frame rate (FPS) by 12%, and significantly reduces the number of parameters and the computational complexity, thereby achieving a balance between performance and efficiency. Two datasets, NEU-DET and APSCC, were employed to evaluate the performance of YOLO-RRL. The results indicate that YOLO-RRL exhibits good adaptability. In comparison to existing mainstream inspection models, YOLO-RRL is also more advanced. The YOLO-RRL model is capable of significantly improving production quality and reducing production costs in practical applications while also extending the scope of the inspection system to a wide range of industrial applications. defect detection; lightweight model; improved algorithm; feature fusion; YOLOv8; DySample model; RepGFPN; RFD model +
Footnote †: journal: Applied Sciences  
As the prevalence of industrial automation and the pace of deep learning technology development increase, the adoption of unmanned processing in all industrial production processes, particularly those related to defect detection, is becoming more prevalent. The necessity for different algorithms in different target objects and application environments in the context of industrial applications is clear. Therefore, algorithms must be selected according to the specific needs of each application and modified to better target different tasks. The existing algorithmic improvement strategies are diverse and focus on optimizing various model components to achieve the desired outcome. [PERSON] et al. [10] introduced a novel algorithm, DEW-YOLO, which combines deformable convolutional networks (DCNs) with the YOLOv8 model, introduces an explicit visual center (EVC) structure, and enhances the defect detection performance by using the Wise-IoU (WIoU) loss function to improve the accuracy of defect detection on steel surfaces. [PERSON] et al. [11] introduced a YOLO (TDD-YOLO) model based on tiny defect detection, which employs a four-layer ME structure in the backbone network and uses miniature detection heads in the head network to improve the accuracy and generalization performance. The model also utilizes a weighted intersection and merger ratio (W-IoU) mechanism to re-evaluate the bounding box regression loss and reduce false alarms. [PERSON] et al. [12] introduced a network called TD-Net for detecting tiny defects in industrial products, which addresses the limitations of current image-based defect detection methods in detecting tiny and differently shaped defects. [PERSON] et al. [13] proposed a framework PE-Transformer to explore the semantic details of small underwater targets using a path-enhanced Transformer detection scheme. [PERSON] et al. [14] introduced a novel approach, DsP-YOLO, which combines an anchorless frame YOLOv8 framework with a lightweight and lightweight YOLOv8 framework. [PERSON] et al. introduced a novel approach, DsP-YOLO, which combines the anchorless YOLOv8 framework with a lightweight and detail-sensitive PAN (DsPAN) to achieve accurate and fast detection of industrial defects. [PERSON] et al. [15] proposed a framework called PCB Defects to address the challenge of accurately identifying defects on printed circuit boards (PCBs).