PCB-YOLO: An Improved Detection Algorithm of PCB Surface Defects Based on YOLOv5

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. 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. 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. Model volume compression is achieved by introducing depth-wise separable convolution. 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. 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.

Automated summary

In this paper, an improved detection algorithm of PCB surface defects based on YOLOv5, named PCB-YOLO, is proposed to address the problems of low network accuracy, slow speed, and a large number of model parameters in PCB defect detection. The algorithm obtains more suitable anchors for the dataset using the K-means++ algorithm and adds a small target detection layer to focus on more small target information. Swin transformer is embedded into the backbone network, and a united attention mechanism is constructed to improve the network's analysis ability. Model volume compression is achieved by introducing depth-wise separable convolution. The EIoU loss function is used to optimize the regression process and enhance the localization ability of small targets. Experimental results show that PCB-YOLO achieves a satisfactory balance between performance and consumption, with 95.97% mAP at 92.5 FPS, making it more accurate and faster than many other algorithms for real-time and high-precision detection of product surface defects.