Printed Circuit Board (PCB) defect detection is essential for ensuring reliability in electronic manufacturing, as defects such as spur, mouse bites, missing holes, and open circuits can severely degrade performance or cause board failure. Traditional AOI systems often suffer from high false alarm rates due to lighting variations and complex backgrounds, while many deep learning methods struggle to efficiently detect tiny defects. To address these challenges, this study proposes SCMEO-DETR, a lightweight and effective end-to-end detector built upon RT-DETR. The model integrates a Sparse Enhanced Pyramid Network (SEPN) and a Context-Guided Feature Pyramid Network (CGFPN) to strengthen fine-grained feature extraction and contextual reasoning. An Optimized and Adaptive Backbone (OA-backbone) and an improved Enhanced Upsampling Convolution Block with Shift Channel Mix (EUCB-SC) block further enhance representational efficiency through adaptive sampling and channel shift mechanisms. An upgraded Multi-Head Multi-Scale Intra-Scale Feature-fusion Module (M2-IFM) module also improves feature interaction. Experiments on the PKU-Market-PCB dataset demonstrate that SCMEO-DETR achieves 97.10% mAP@0.5, surpassing RT-DETR-R18 by 5.28% while reducing parameters and GFLOPs by 50% and 39%. These results confirm its effectiveness and deployability for real-time PCB defect detection.
Li et al. (Tue,) studied this question.