iipp publishingJournal of Applied Engineering Science

EFFECTIVE IMAGE MODELS FOR INSPECTing PROFILE FLAWS OF CAR MIRRORS WITH APPLICATIONS


DOI: 10.5937/jaes18-22825
This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions. 
Creative Commons License

Volume 18 article 662 pages: 81 - 91

Yuan-Shyi Peter Chiu
Chaoyang University of Technology, Taichung, Taiwan

Yu-Kai Lin
Chaoyang University of Technology, Taichung, Taiwan

Hong-Dar Lin*
Chaoyang University of Technology, Taichung, Taiwan

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 fl aws of car mirrors include: scratches, bubbles, pinholes causing surface fl aw type and burrs,damaged edges causing profi le fl aw type. Currently, the inspection tasks are conducted by human inspectors. Since the profi le fl aws will cause structural damages of car mirrors and reduce ability to withstand stress, the level of damage suffered even more than the surface fl aws. In addition, the angle diversity of capturing images makes it hard to implement automatic optical inspection. Therefore, this study develops an automated profi le fl aw detection system for car mirrors to replace visual inspection personnel. This study proposes a self-contrast defect detection method for the profi le fl aw inspection of car mirrors. It is not required to provide a standard fl awless sample in detection process and derive information compared with testing samples. The proposed method fi rst extracts the contour information of the testing image by Fourier descriptors. Then, after some middle and high-frequency coeffi cients were fi ltered 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 classifi cation rate is up to 97.47%.
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This study was partially sponsored by the Ministry of Science and Technology, Taiwan, for the financial support through the Grant MOST 105-2221-E-324-013-MY2.

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