An application of rough set theory to defect detection of automotive glass
A technique based on rough set theory is investigated for identifying defects on a backlight (a rear window of a vehicle with a defrost circuit). Since replacement of defective backlights result in a significant financial loss, automobile manufacturers are trying to remove defective backlights during the manufacturing process. Therefore, an automated inspection system based on infrared (IR) imaging techniques has been developed to detect backlight defects such as missing lines or hotspots, where the most challenging task is identifying hotspots from their artifacts.
Year of publication: |
2002
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Authors: | Lee, Seungkoo ; Vachtsevanos, George |
Published in: |
Mathematics and Computers in Simulation (MATCOM). - Elsevier, ISSN 0378-4754. - Vol. 60.2002, 3, p. 225-231
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Publisher: |
Elsevier |
Subject: | Rough set theory | Automated inspection system | Feature selection | Rule generation | Automotive glass |
Saved in:
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