An Improved YOLOv5s for Detecting Glass Tube Defects

Zhibo Wei, Liying Zheng

Abstract: Existing algorithms for detecting glass tube defects suffer from low recognition accuracy and huge scales. This paper proposes an improved YOLOv5s to solve these problems. Specifically, the Convolutional Block Attention Module (CBAM) is used in the improved YOLOv5s to enhance the feature extraction. Then, the Content-Aware ReAssembly of FEatures (CARAFE) is used to replace the original upsampling method, which is beneficial for feature fusion. Next, the Efficient Intersection over Union (EIoU) loss is substitute to the original Complete Intersection over Union (CIoU) loss of YOLOv5s, which improves the regression accuracy. Finally, we adopt Cosine Warmup to accelerate the convergence as well as improve the detection performance. The experimental results show that, compared with the original YOLOv5s, our improved YOLOv5s increases the mean Average Precision (mAP) by 8% while decreasing the amount of model parameters by 5.8%. Moreover, the improved detector reaches 98 Frames Per Second (FPS) that meets the requirement of real-time detection.