Real-Time Wheel Detection and Rim Classification in Automotive Production

0575756 - ÚTIA 2024 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Staněk, R. - Kerepecký, Tomáš - Novozámský, Adam - Šroubek, Filip - Zitová, Barbara - Flusser, Jan
Real-Time Wheel Detection and Rim Classification in Automotive Production.
Proceedings of the 2023 IEEE International Conference on Image Processing (ICIP). Piscataway: IEEE, 2023, s. 1410-1414. ISBN 978-1-7281-9835-4.
[IEEE International Conference on Image Processing 2023 (ICIP 2023). Kuala Lumpur (MY), 08.10.2023-11.10.2023]
Grant CEP: GA ČR GA21-03921S
Grant ostatní: AV ČR(CZ) StrategieAV21/1
Institucionální podpora: RVO:67985556
Klíčová slova: Detection * Classification * Automotive
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://library.utia.cas.cz/separaty/2023/ZOI/kerepecky-0575756.pdf

This paper proposes a novel approach to real-time automatic rim detection, classification, and inspection by combining traditional computer vision and deep learning techniques. At the end of every automotive assembly line, a quality control process is carried out to identify any potential defects in the produced cars. Common yet hazardous defects are related, for example, to incorrectly mounted rims. Routine inspections are mostly conducted by human workers that are negatively affected by factors such as fatigue or distraction. We have designed a new prototype to validate whether all four wheels on a single car match in size and type.
Additionally, we present three comprehensive open-source databases, CWD1500, WHEEL22, and RB600, for wheel, rim, and bolt detection, as well as rim classification, which are free-to-use for scientific purposes.
Trvalý link: https://hdl.handle.net/11104/0345841