![]() proposed an automatic detection algorithm for PCB pinholes by combining machine learning knowledge. To solve the problems, some scholars have introduced machine learning into PCB detection and have made great progress. Additionally, the detection of tiny defects can cause visual fatigue and lead to misclassification. Traditional manual inspection is easily disrupted by external environmental factors, which can affect the efficiency of defect detection. The experimental results show that PCB-YOLO achieves a satisfactory balance between performance and consumption, reaching 95.97% mAP at 92.5 FPS, which is more accurate and faster than many other algorithms for real-time and high-precision detection of product surface defects. The EIoU loss function is used to optimize the regression process of the prediction frame and detection frame, which enhances the localization ability of small targets. Model volume compression is achieved by introducing depth-wise separable convolution. ![]() Swin transformer is embedded into the backbone network, and a united attention mechanism is constructed to reduce the interference between the background and defects in the image, and the analysis ability of the network is improved. ![]() ![]() Based on the K-means++ algorithm, more suitable anchors for the dataset are obtained, and a small target detection layer is added to make the PCB-YOLO pay attention to more small target information. ![]() To address the problems of low network accuracy, slow speed, and a large number of model parameters in printed circuit board (PCB) defect detection, an improved detection algorithm of PCB surface defects based on YOLOv5 is proposed, named PCB-YOLO, in this paper. ![]()
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