Abstract: This paper presents a learning-based visual inspection met hod that addresses the need for an improved adaptability ofa visual inspection system for parts verification in panorama sunroof assembly lines. It is essential to ensure that the manyparts required (bolts and nuts, etc.) are properly installed in the PLC sunroof manufacturing process. Instead of humaninspectors, a visual inspection system can automatically perform parts verification tasks to assure that parts are properly installedwhile rejecting any that are improperly assembled. The proposed visual inspection method is able to adapt to changinginspection tasks and environmental conditions through an efficient learning process. The proposed system consists of two majormodules: learning mode and test mode. The SVM (Support Vector Machine) learning algorithm is employed to implement partlearning and verification. The proposed method is very robust for changing environmental conditions, and various experimentalresults show the effectiveness of the proposed method.Keywords: visual inspection, part verification, panorama sunroof, support vector machine
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