Sifting Method of Defect Candidate on Coated Automobile Roofs based on Binarization and Brightness Difference

Abstract

Defects in automotive coated surfaces have a significant impact on consumers' purchase decisions. At present, most of the global automotive companies still rely on visual inspection to detect defects. With the development of industry4.0, in order to reduce the burden on inspectors, an inspection device is needed to help inspectors work more effectively. A defect detection system using a single camera, which filters the defect candidates using the tracking trajectories of the defect candidates on multiple frames has already proposed. However, this method has many noises for metallic color coated surfaces. This paper presents a new method to sift the defect candidates based on binarization and brightness difference. The experimental results demonstrate that this method can more effectively suppress the negative effects of sifting defect candidates. In the experiment, the F-measure are 100% for the coated surface.