An Improved YOLOv5 Network for Detection of Printed Circuit Board Defects

Abstract

With the rapid development of China’s printed circuit board industry, bare-board defect detection has high research and application values as an important factor in improving production quality. In this paper, a new detection method based on YOLOv5 is proposed to solve the balance problem of efficiency and performance in the task of circuit board defect detection. First, the $k$ -means++ method is used to improve the location matching of the prior anchor boxes. Second, the Focal-EIOU loss function is used instead of GIOU to address the former’s degeneration issue. Finally, the ECA-Net module is added to enhance the sensitivity of the model to channel features. Experiments were conducted on a public defect dataset, and superior performance was achieved. The proposed method achieves 99.1% mean average precision at 86 frames per second. Compared with other advanced methods, the proposed method achieves the highest mean average precision value, and the detection speed allows real-time applications.