Abstract Defect detection is important in quality assurance on production lines. This paper presents a fast machine-vision-based surface defect detection method using the weighted least-squares model. We assume that an inspection image can be regarded as a combination of a defect-free template image and a residual image. The defect-free template image is generated from training samples adaptively, and the residual image is the result of the subtraction between each inspection image and corresponding defect-free template image. In the weighted least-squares model, the residual error near the edge is suppressed to reduce the false alarms caused by spatial misalignment. Experiment results on different types of buttons show that the proposed method is robust to illumination vibration and rotation deviation and produces results that are better than those of two other methods.
Keywordsmachine vision surface defect detection weighted least-squares model
Yubin Wu is an associate professor in the School of Optical and Electronic Information, Huazhong University of Science and Technology. He received his M.E. degree in optical engineering from Institute of Optics and Electronics of the Chinese Academy of Sciences in 1987. He received his B.E. degree in optical instruments from Huazhong University of Science and Technology in 1984. His research interests include optoelectronic sensing and signal processing, machine vision, and development of high-tech products.
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