Abstract: To address the demand for surface defect detection in PCB production, a defect classification standard encompassing 11 categories was established based on actual workshop conditions, images of real PCB surface defects were collected, and finally a dataset named Dataset_PCBSD was constructed, containing 3 239 images with 4 672 defective objects. A new PCB surface defect detection model, YOLOv5s-P6SE, was developed based on improvements to YOLOv5s. To enhance detection accuracy, a P6 detection layer for detecting extremely large objects was added to YOLOv5s, along with the introduction of the SE attention module and soft non-maximum suppression post-processing. Experimental results show that YOLOv5s-P6SE achieves a 5.5% improvement in mean average precision (mAP) compared to the baseline model YOLOv5s. Additionally, YOLOv5s-P6SE outperforms Faster R-CNN, SSD, the PCB defect detection model YOLOv4-MN3, and the DETR model RT-DETR-L in terms of both mAP and model size. It also excels in balancing mAP and model size compared to YOLOv8s. 

Key words: printed circuit board, surface defect detection, YOLOv5s-P6SE, SE attention module, soft non-maximum suppression