YOLO-EF: A lightweight YOLOv7 PCB defect detection algorithm

Abstract This study addresses the challenge of accurate PCB defect detection, considering the computational complexity and excessive model parameters associated with existing methods. To mitigate these issues, we propose an enhanced PCB surface defect detection method called YOLO-EF, which is built upon the YOLOv7 architecture. Firstly, we replace the backbone of YOLOv7 with the lightweight Efficient FormerV2 module. This replacement reduces both the model volume and computational requirements. Secondly, the dynamic head module is introduced to unify the target detection head and improve its performance. Finally, the Wise-IoUv3 function is used to suppress the influence of low-quality samples on the model performance. The experimental results show that the proposed model improves by 2.85% in mAP over the original model. Model computation is reduced from 106.5 GFLOPs to 44.3 GFLOPs. Our YOLO-EF model achieves an optimal balance in terms of precision, computational efficiency, and detection speed.