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Unsupervised accumulated aggregation shifting for robust defect detection in real industry
Author(s): Yaping Yan; Shun’ichi Kaneko
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Paper Abstract

More and more electronic products use non-smooth surfaces to improve user experience. Computer vision based quality control is very important for these products. Since images taken from the non-smooth surfaces have complex micro textures and low contrasts, it is very challenging to detect defects in the images. To solve this problem, this paper proposes a training-free method which first enhances defects and then detect them accurately. At the phase of defect enhancement, pixel-level saliency is first calculated by two novel features named localglobal intensity difference and local intensity aggregation, and then an iterative enhancement approach named accumulated aggregation shifting (AAS) is proposed to shift each pixel’s intensity according to its saliency. At the phase of defect detection, two statistic models, including linear distribution or exponential distribution, are fitted by the shifting results of AAS at different iterations. Based on the fitted statistic models, defective pixels are defect-free pixels are accurately classified by risk minimization. Experimental results prove that the proposed approach is effective in detecting defects on non-smooth surfaces of real industrial products.

Paper Details

Date Published: 16 July 2019
PDF: 7 pages
Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 1117212 (16 July 2019); doi: 10.1117/12.2521748
Show Author Affiliations
Yaping Yan, Hokkaido Univ. (Japan)
Shun’ichi Kaneko, Hokkaido Univ. (Japan)


Published in SPIE Proceedings Vol. 11172:
Fourteenth International Conference on Quality Control by Artificial Vision
Christophe Cudel; Stéphane Bazeille; Nicolas Verrier, Editor(s)

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