YOLO-SSW: An Improved Detection Method for Printed Circuit Board Surface Defects

Accurately recognizing tiny defects on printed circuit boards (PCBs) remains a significant challenge due to the abundance of small targets and complex background textures. To tackle this issue, this article proposes a novel YOLO-SPD-SimAM-WIoU (YOLO-SSW) network, based on an improved YOLOv8 algorithm, to detect tiny PCB defects with greater accuracy and efficiency. Firstly, a high-resolution feature layer (P2) is incorporated into the feature fusion part to preserve detailed spatial information of small targets. Secondly, a Non-strided Convolution with Space-to-Depth (Conv-SPD) module is incorporated to retain fine-grained information by replacing traditional strided convolutions, which helps maintain spatial resolution. Thirdly, the Simple Parameter-Free Attention Module (SimAM) is integrated into the backbone to enhance feature extraction and noise resistance, focusing the model’s attention on small targets in relevant areas. Finally, the Wise-IoU (WIoU) loss function is adopted to dynamically adjust gradient gains, reducing the impact of low-quality examples, thereby enhancing localization accuracy. Comprehensive evaluations on publicly available PCB defect datasets have demonstrated that the proposed YOLO-SSW model significantly outperforms several state-of-the-art models, achieving a mean average precision (mAP) of 98.4%. Notably, compared to YOLOv8s, YOLO-SSW improved the mAP, precision, and recall by 0.8%, 0.6%, and 0.8%, respectively, confirming its accuracy and effectiveness.