An improved YOLOv5-based model for automatic PCB defect detection

Abstract

The detection of PCB defects plays an important role in PCB production. To meet the requested quality standard, systematic research was invested in PCB defect detection. The existing PCB defect detection methods are mainly trained by using an artificial defect image dataset, which is much more ideal than the real PCB production process. To deal with the real PCB production defect detection, we collected 3239 image samples and labeled each of them by label-image tool. This paper proposed a new model improved from the basic YOLOv5 framework, adding a scale and self-attention mechanism. The results revealed that this model performed well on defect location and classification with its mAP0.5 reaching 63.4%.