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Vehicle wheel weld detection based on improved YOLO v4 algorithm
T.J. Liang 1,2, W.G. Pan 1,2, H. Bao 1,2, F. Pan 1,2

Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China,
College of Robotics, Beijing Union University, Beijing, China

 PDF, 2233 kB

DOI: 10.18287/2412-6179-CO-887

Pages: 271-279.

Full text of article: English language.

In recent years, vision-based object detection has made great progress across different fields. For instance, in the field of automobile manufacturing, welding detection is a key step of weld inspection in wheel production. The automatic detection and positioning of welded parts on wheels can improve the efficiency of wheel hub production. At present, there are few deep learning based methods to detect vehicle wheel welds. In this paper, a method based on YOLO v4 algorithm is proposed to detect vehicle wheel welds. The main contributions of the proposed method are the use of k-means to optimize anchor box size, a Distance-IoU loss to optimize the loss function of YOLO v4, and non-maximum suppression using Distance-IoU to eliminate redundant candidate bounding boxes. These steps improve detection accuracy. The experiments show that the improved methods can achieve high accuracy in vehicle wheel weld detection (4.92 % points higher than the baseline model with respect to AP75 and 2.75 % points higher with respect to AP50). We also evaluated the proposed method on the public KITTI dataset. The detection results show the improved method’s effectiveness.

object detection, vehicle wheel weld, YOLO v4, DIoU.

Liang TJ, Pan WG, Bao H, Pan F. Vehicle wheel weld detection based on improved YOLO v4 algorithm. Computer Optics 2022; 46(2): 271-279. DOI: 10.18287/2412-6179-CO-887.

The work was funded by the National Natural Science Foundation of China (Nos. 61802019, 61932012, 61871039) and the Beijing Municipal Education Commission Science and Technology Program (Nos. KM201911417009, KM201911417003, KM201911417001). Beijing Union University Research and Innovation Projects for Postgraduates (No.YZ2020K001).


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