Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion

Abstract

In printed circuit board (PCB) defect detection, it is difficult to collect defect samples, and the detection effect is poor due to the lack of data. On the basis of the few-shot learning method, a few-shot PCB defect detection model is proposed. This model introduces feature enhancement module and multi-scale fusion module. The feature enhancement module based on the improved convolution block attention module (CBAM) can highlight the key areas of the received feature maps and suppress the interference of useless information. Aiming at the small size of PCB defects, a multi-scale feature fusion strategy is proposed. It can extract multi-scale feature maps of PCB and fuse them into a high-quality feature map containing different scale information, which can improve the detection precision of the model for small object defects. A large number of experiments on PCB dataset show that our few-shot PCB defect detection model outperforms state-of-the-art methods under different shot settings ( $\text{k}=1$ ,2,3,5,10,30). Notably, the proposed model can take into account both detection efficiency and precision, which means it has high practical application value.

Keywords

• PCB defect detection
• few-shot learning
• feature enhancement
• multi-scale fusion