EFFECTIVE IMAGE MODELS FOR INSPECTING PROFILE FLAWS OF CAR MIRRORS WITH APPLICATIONS
Abstract
Since car mirrors are standard accessories with cars, the demands of car mirrors are 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 flaw 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 degree of harm even more than the surface flaws. In additions, the angle diversity of capturing images makes it is not easy to implement automatic optical inspection. Therefore, this study develops an automated profile flaw detection system of car mirrors to replace visual inspection personnel from car mirror inspection tasks. 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. We first extract 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 flaw districts are easily separated by image subtraction. Experimental consequences demonstrate that the flaw inspection rate reaches to 85.05%, and the incorrect alert rate is smaller 0.07%, and the correct classification rate is up to 97.47%.
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