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Author

Zhenxin Yuan

Bio: Zhenxin Yuan is an academic researcher from University of Jinan. The author has contributed to research in topics: Object detection. The author has co-authored 1 publications.

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Zhenxin Yuan1, Shuai Zhao, Fu Zhao, Tao Ma, Zhongtao Li1 
22 Oct 2021
TL;DR: Wang et al. as mentioned in this paper proposed an automobile rim weld detection algorithm YOLOv4-head2-BiFPN on the basis of YOLOOv4 algorithm to ensure high accuracy and speed of detection, which does not affect the detection speed by strengthening feature fusion and removing redundant detection heads.
Abstract: At present, the production efficiency of automobile rim in the industrial field is affected by the detection process of automobile rim quality after steel forging. The traditional way is to check welding position manually, which can facilitate the air tightness detection after weld is pressurized. However, this can largely affect production efficiency. By introducing computer vision and image processing, the position of the rim weld can be accurately located, which is more accurate and time-saving. In order to ensure high accuracy and speed of detection, we propose an automobile rim weld detection algorithm YOLOv4-head2-BiFPN on the basis of YOLOv4 algorithm. The experimental results show that, for one thing, it does not affect the detection speed by strengthening feature fusion and removing redundant detection heads. For another, the AP75 of the improved YOLOv4-head2-BiFPN algorithm in the automobile rim weld detection task is 7.7% higher than that of the original YOLOv4 algorithm.