Article
Version 1
Preserved in Portico This version is not peer-reviewed
Application Study of YOLOv5 Algorithm on Automotive Wheel Surface Defect Detection
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. 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. Preprints 2023, 2023061069. 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.
Keywords
wheel surface defect detection; deep learning; YOLO; object detection; machine vision
Subject
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.
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