An Efficient and Robust Target Detection Algorithm for Identifying Minor Defects of Printed Circuit Board Based on PHFE and FL-RFCN

Frontiers in Physics

Qi Wang¹  Siyu Xia¹  Fan Wang²  Xu Ling²  Fei Xie²  Lei Huang³ 

[1] School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China;School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China;Jiangsu Province 3D Printing Equipment and Manufacturing Key Lab, Nanjing, China;School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China;

关键词: printed circuit board;    minor defect;    data enhancement;    focal loss;    high-definition feature extraction;   

DOI  :  10.3389/fphy.2021.661091

来源: Frontiers

【 摘 要 】

For ensuring the safety and reliability of electronic equipment, it is a necessary task to detect the surface defects of the printed circuit board (PCB). Due to the smallness, complexity and diversity of minor defects of PCB, it is difficult to identify minor defects in PCB with traditional methods. And the target detection method based on deep learning faces the problem of imbalance between foreground and background when detecting minor defects. Therefore, this paper proposes a minor defect detection method on PCB based on FL-RFCN (focal loss and Region-based Fully Convolutional Network) and PHFE (parallel high-definition feature extraction). Firstly, this paper uses the Region-based Fully Convolutional Network(R-FCN) to identify minor defects on the PCB. Secondly, the focal loss is used to solve the problem of data imbalance in neural networks. Thirdly, the parallel high-definition feature extraction algorithm is used to improve the recognition rate of minor defects. In the detection of minor defects on PCB, the ablation experiment proves that the mean Average accuracy (mAP) of the proposed method is increased by 7.4. In comparative experiments, it is found that the mAP of the method proposed in this paper is 12.3 higher than YOLOv3 and 6.7 higher than Faster R-CNN.

【 授权许可】

CC BY