Enhancing YOLOv5 for superior PCB defect detection: lightweight heads and small target precision

This paper introduces an improved YOLOv5 network designed to achieve enhanced performance in the field of printed circuit board (PCB) defect detection. We incorporate a lightweight detection head, reducing the number of network parameters and improving computational efficiency. This not only facilitates operation in resource-constrained environments but also maintains detection accuracy. To better address the specific requirements of PCB defect detection, we introduce a small target detection head. By adding a detection head specifically designed for small targets, we enhance the network’s ability to perceive subtle defects, thereby improving detection accuracy. Finally, we validate our proposed improvements through experiments on a large-scale PCB defect dataset. Compared to the traditional YOLOv5 network, our model exhibits significant improvements in both accuracy and efficiency.