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. Feature selection techniques based on rough set theory are explored in this paper and are used to extract a feature vector, which increases inspection accuracy as well as reduces computational complexity. The theory is also applied to generate decision rules, which can be simply added to existing inspection systems to assist the operators in their decision making process. The proposed inspection system is expected to provide more reliable fault detection with low rate of false alarms than currently available systems. © 2002 IMACS. Published by Elsevier Science B.V. All rights reserved.