Defect Detection for Metal Base of TO-Can Packaged Laser Diode Based on Improved YOLO Algorithm

Abstract

Defect detection is an important part of the manufacturing process of mechanical products. In order to detect the appearance defects quickly and accurately, a method of defect detection for the metal base of TO-can packaged laser diode (metal TO-base) based on the improved You Only Look Once (YOLO) algorithm named YOLO-SO is proposed in this study. Firstly, convolutional block attention mechanism (CBAM) module was added to the convolutional layer of the backbone network. Then, a random-paste-mosaic (RPM) small object data augmentation module was proposed on the basis of Mosaic algorithm in YOLO-V5. Finally, the K-means++ clustering algorithm was applied to reduce the sensitivity to the initial clustering center, making the positioning more accurate and reducing the network loss. The proposed YOLO-SO model was compared with other object detection algorithms such as YOLO-V3, YOLO-V4, and Faster R-CNN. Experimental results demonstrated that the YOLO-SO model reaches 84.0% mAP, 5.5% higher than the original YOLO-V5 algorithm. Moreover, the YOLO-SO model had clear advantages in terms of the smallest weight size and detection speed of 25 FPS. These advantages make the YOLO-SO model more suitable for the real-time detection of metal TO-base appearance defects.

Keywords

• defect detection
• deep learning
• YOLO
• small object detection
• computer vision
• attention mechanism