Effective image models for inspecting profile flaws of car mirrors with applications

Abstract

Since car mirrors are standard accessories with cars, the demand for car mirrors is growing and manufacturers also pay more emphasis on the increase of product quality. Common appearance flaws of car mirrors include: scratches, bubbles, pinholes causing surface fl aw type and burrs,damaged edges causing profile flaw type. Currently, the inspection tasks are conducted by human inspectors. Since the profile flaws will cause structural damages of car mirrors and reduce ability to withstand stress, the level of damage suffered even more than the surface flaws. In addition, the angle diversity of capturing images makes it hard to implement automatic optical inspection. Therefore, this study develops an automated profile flaw detection system for car mirrors to replace visual inspection personnel. This study proposes a self-contrast defect detection method for the profile flaw inspection of car mirrors. It is not required to provide a standard flawless sample in detection process and derive information compared with testing samples. The proposed method first extracts the contour information of the testing image by Fourier descriptors. Then, after some middle and high-frequency coefficients were filtered out, an approximated contour image can be rebuilt from the Fourier domain for comparing with the testing image. Finally, the fl aw districts are easily separated by image subtraction. Experimental results demonstrate that the fl aw inspection rate reaches 85.05%, and the incorrect alert rate is smaller than 0.07%, and the correct classification rate is up to 97.47%.