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

Siyu Xia,Fan Wang,Qi Wang,Lei Huang,Xu Ling,Fei Xie

doi:10.3389/fphy.2021.661091

Abstract

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.

Highlights

printed circuit board (PCB) defect detection based on machine vision usually can only detect surface defects
Since all minor defects of printed circuit boards are small targets, this paper proposes a PHFE method to improve the recognition rate of minor defects
We propose a parallel high-definition feature extraction method, in which the bottom layer features are added every time the feature layers of different scales are fused, so that the features at the minor defects always exist in the feature map

Summary

INTRODUCTION

PCB defect detection based on machine vision usually can only detect surface defects. Since similar minor defects will cause classification difficulties, this is a data imbalance problem, we need to choose a suitable algorithm to solve the impact of. An Efficient Target Detection Algorithm sample imbalance on the recognition rate of minor defects [5]. We use multi-scale fusion and focal loss to optimize R-FCN to detect and identify minor defects. The main contributions of this article are as follows: 1) The minor defect area occupies a tiny area of the entire printed circuit board, which makes the target detection algorithm produce data imbalance when classifying foreground and background. Through adaptively adjusting the weight of background and foreground during training, the proposed method solves the problem of data imbalance caused by minor defects. The proposed method improves the ability of neural network to recognize minor defects

PROPOSED METHOD

EXPERIMENTS AND ANALYSIS

Tests and Results

DATA AVAILABILITY STATEMENT

CONCLUSION