Research on Low-Cost Non-Contact Vision-Based Wheel Arch Detection for End-of-Line Stage.

Zhigang Ding, Mingsheng Lin, Yi Ding, Yun Li, Qincheng Zhang

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Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Zhigang DingSchool of Mechanical and Automotive Engineering, Fujian University of Technology, Fuzhou 350118, China.

Mingsheng LinSchool of Mechanical and Automotive Engineering, Fujian University of Technology, Fuzhou 350118, China.

Yi DingSchool of Mechanical and Automotive Engineering, Fujian University of Technology, Fuzhou 350118, China.

Yun LiSchool of Mechanical and Automotive Engineering, Fujian University of Technology, Fuzhou 350118, China.

Qincheng ZhangSchool of Mechanical and Automotive Engineering, Fujian University of Technology, Fuzhou 350118, China.

Funding

National Natural Science Foundation of China 62376059

6 · The paper itself

Abstract

To address the collaborative requirements of high precision, high efficiency, low cost, and non-contact measurement for wheel arch detection in the calibration of Advanced Driver Assistance Systems (ADAS) during vehicle production, this study proposes a monocular machine vision-based detection methodology. The hardware system incorporates an industrial camera, priced at approximately 1000 CNY, and a custom light source. The YOLOv5s model is employed for rapid localization of the wheel hub, while the MSER algorithm, in conjunction with Canny edge detection, is utilized for robust feature extraction of the wheel arch. A geometric computation model, referenced to the wheel hub, is subsequently established to quantify the wheel arch height. Experimental results indicate that, for seven vehicle models, the method achieves an average absolute error (MAE) of ≤0.25 mm, with a maximum error of ≤0.545 mm and a single measurement time of ≤3.2 s, making it suitable for a 60 JPH production line. Additionally, under lighting conditions ranging from 500 to 1500 lux and dust concentrations of ≤10 mg/m

Indexed as

low costmachine visionnon-contact measurementvehicle end-of-linewheel arch detection

Identifiers

PMID41516668

DOI10.3390/s26010234

PMCPMC12788266

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