Version 1
: Received: 14 June 2023 / Approved: 15 June 2023 / Online: 15 June 2023 (07:20:42 CEST)
How to cite:
Xia, X.; Zhang, Z.; Liu, F. Application Study of YOLOv5 Algorithm on Automotive Wheel Surface Defect Detection. Preprints2023, 2023061069. https://doi.org/10.20944/preprints202306.1069.v1
Xia, X.; Zhang, Z.; Liu, F. Application Study of YOLOv5 Algorithm on Automotive Wheel Surface Defect Detection. Preprints 2023, 2023061069. https://doi.org/10.20944/preprints202306.1069.v1
Xia, X.; Zhang, Z.; Liu, F. Application Study of YOLOv5 Algorithm on Automotive Wheel Surface Defect Detection. Preprints2023, 2023061069. https://doi.org/10.20944/preprints202306.1069.v1
APA Style
Xia, X., Zhang, Z., & Liu, F. (2023). Application Study of YOLOv5 Algorithm on Automotive Wheel Surface Defect Detection. Preprints. https://doi.org/10.20944/preprints202306.1069.v1
Chicago/Turabian Style
Xia, X., Zhenyu Zhang and Fucai Liu. 2023 "Application Study of YOLOv5 Algorithm on Automotive Wheel Surface Defect Detection" Preprints. https://doi.org/10.20944/preprints202306.1069.v1
Abstract
Surface defect detection is a crucial step in the process of automotive wheel production. However, the task possesses challenges due to complex background and a wide range of defect types. In order to detect the defects on the wheel surface accurately and quickly, this paper proposes a YOLOv5-based algorithm for automotive wheel surface defect detection. The algorithm trains and tests the YOLOv5s model using the self-created automotive wheel surface defect dataset, which contains four kinds of defects: linear, dotted, sludge, pinhole. The extensive experimental results demonstrate that the deep learning network trained by our method can achieve an average accuracy of 71.7% and 57.14 FPS. Our findings prove that this detection algorithm performs better than other common target detection algorithms and meets the real-time requirements of industrial applications.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
