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Defect detection on button surfaces with the weighted least-squares model

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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.

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Authors and Affiliations

  1. School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yu Han, Yubin Wu, Danhua Cao & Peng Yun

Authors
  1. Yu Han
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  2. Yubin Wu
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  3. Danhua Cao
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  4. Peng Yun
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Corresponding author

Correspondence to Danhua Cao.

Additional information

Yu Han received his B.E. degree in the School of Optical and Electronic Information, Huazhong University of Science and Technology, in 2014. He is currently pursuing his M.E. degree in the School of Optical and Electronic Information, Huazhong University of Science and Technology. His research interests include machine vision and image processing.

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.

Danhua Cao is a professor in the School of Optical and Electronic Information, Huazhong University of Science and Technology. She received her Ph.D. degree in electronic physics and devices from Huazhong University of Science and Technology in 1993. She received her B.E. degree in measuring and control technology and instrumentations from Huazhong University of Science and Technology in 1987. She is a permanent member of the Professional Committee of Opto-electronic Technology in the Chinese Optical Society. Her research interests include optoelectronic sensing and signal processing as well as machine vision algorithms and systems.

Peng Yun is a senior student in the School of Optical and Electronic Information, Huazhong University of Science and Technology. During his undergraduate study, he was active in the field of machine vision and machine learning. He has joined Robotics and Multiperception Laboratory, where he conducts research on cloud-based simultaneous localization and mapping algorithms. His research interests include machine vision, machine learning, and SLAM.

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Han, Y., Wu, Y., Cao, D. et al. Defect detection on button surfaces with the weighted least-squares model. Front. Optoelectron. 10, 151–159 (2017). https://doi.org/10.1007/s12200-017-0687-7

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  • DOI: https://doi.org/10.1007/s12200-017-0687-7

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