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Proceedings Paper

A surface defect detection method based on multi-feature fusion
Author(s): Xiaojun Wu; Huijiang Xiong; Zhiyang Yu; Peizhi Wen
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Paper Abstract

Automatic inspection takes a great role in guaranteeing the product quality. But one of the limitations of current inspection algorithms is either product specific or problem specific. In this paper, we propose a defect detection method based on three image features fusion for variety of industrial products surface detection. The proposed method learns sub-image gray level difference, color histogram and pixel regularity of qualified images off-line and test the images based on the detection results of these three image features. It avoids the feature training of defect products as it is difficult to collect large amount of defect samples. The experimental results show that the detection accuracy is between 93% and 98% and the approach is efficient for the real time applications of industrial product inspect.

Paper Details

Date Published: 21 July 2017
PDF: 6 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104200S (21 July 2017); doi: 10.1117/12.2282188
Show Author Affiliations
Xiaojun Wu, Harbin Institute of Technology (China)
Shenzhen Key Lab. for Advanced Motion Control and Modern Automation Equipment (China)
Huijiang Xiong, Harbin Institute of Technology (China)
Zhiyang Yu, Harbin Institute of Technology (China)
Peizhi Wen, Guilin Univ. of Electronic Technology (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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