Single‐shot detector with enriched semantics for PCB tiny defect detection

Abstract

Printed circuit board (PCB) defect detection is one of the primary problems in quality control of the most electronic products. Usually, the industrial PCB imagery has high resolution, but defects take up a small proportion (often only ∼10 pixels in size), which makes it difficult to use traditional machine vision methods. To this end, a novel single shot object detector (SSDT) is proposed for tiny defect detection in PCBs in this study. Specifically, a semantic ascending module, which propagates the semantic property of deep layers to shallow layers, is presented by fusing features of different levels. An attention mechanism is utilised to learn the relationship of the features to be fused across channels and a shuffle module is used to eliminate the aliasing effect after fusion. Moreover, the improved non-maximum suppression is proposed to extenuate the overlap effect for testing the whole PCB image. The proposed detector can rapidly detect tiny defects and the results of SSD and SSDT are further compared not only in PCB defect dataset but also the object detection public dataset PASCAL VOC2007 where SSDT achieves 81.3% mAP, better than SSD (79.5%). In final, the proposed detector is validated to be robust to rotation and blur.