Improving PCB defect detection using selective feature attention and pixel shuffle pyramid

Abstract

Due to the ongoing miniaturization of electronic products and the use of miniature printed circuit boards (PCBs), existing AI-based defect detection methods have exhibited poor performance in detecting tiny PCB defects. This issue can potentially compromise safety, degrade manufacturing quality, and increase production costs. To tackle this problem, we propose two novel techniques for PCB defect detection, namely Selective Feature Attention (SF attention) and Pixel Shuffle Pyramid (PSPyramid). SF attention identifies important features from a pyramid feature map to fuse the semantic and spatial information, while PSPyramid effectively fuses semantic features to detect various types of defects on PCBs, especially tiny defects. Moreover, a customized training strategy, specifically for PCB defect detection, is devised. To evaluate the performance of our proposed algorithms, extensive experiments have been conducted on two well-known PCB datasets containing tiny defects: the DeepPCB and TDD datasets. Our proposed non-referential method achieves performance comparable to existing referential methods on the DeepPCB dataset, making it more feasible for industrial applications. Compared to state-of-the-art methods, our method reduces the error by 16%, in terms of AP50, on the TDD dataset. The experimental results demonstrate the effectiveness of our proposed method in improving the quality assurance process for PCBs in the electronics industry.