Defect Detection Method of Phosphor in Glass Based on Improved YOLO5 Algorithm

Abstract

Phosphor in Glass (PiG) is easy to be stirred unevenly during production and processing, and improper use of instruments and other factors lead to defective products. In this paper, we propose an improved YOLOv5 target detection algorithm. Firstly, the Coordinate Attention (CA) is introduced into the backbone network to enable the network to notice detect targets in a larger range. Secondly, the Bidirectional Feature Pyramid Network (BiFPN) is used to fuse different scale information in the neck part to obtain the output feature map with rich semantic information. At the same time, the weighted bidirectional feature fusion pyramid structure adjusts the contribution of different scale input feature maps to the output by introducing weights. This optimization enhances the feature fusion effect, reduces the loss of feature information in the convolution process, and improves detection accuracy. Then, the GIOU_Loss function is replaced with the EIOU_Loss function to speed up the convergence. Finally, the comparative experiment is carried out with the self-made PiG dataset. The experimental results show that the average accuracy mAP of this method is 12.35% higher than that of the original method (YOLOv5s), with a detection speed is 53.92 FPS, aligning with the actual needs of industrial detection.