Real-Time Wheel Detection and Rim Classification in Automotive Production

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...

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Bibliographic Details
Published in2023 IEEE International Conference on Image Processing (ICIP) pp. 1410 - 1414
Main Authors Stanek, Roman, Kerepecky, Tomas, Novozamsky, Adam, Sroubek, Filip, Zitova, Barbara, Flusser, Jan
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.10.2023
Subjects
Automotive
Classification
Computer vision
Deep learning
Detection
Inspection
Real-time systems
Support vector machines
Transforms
Wheels
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Summary: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.
DOI:10.1109/ICIP49359.2023.10223161