21 July 2017 A surface defect detection method based on multi-feature fusion
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104200S (2017) https://doi.org/10.1117/12.2282188
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
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.
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Xiaojun Wu, Xiaojun Wu, Huijiang Xiong, Huijiang Xiong, Zhiyang Yu, Zhiyang Yu, Peizhi Wen, Peizhi Wen, } "A surface defect detection method based on multi-feature fusion", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104200S (21 July 2017); doi: 10.1117/12.2282188; https://doi.org/10.1117/12.2282188
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