Unsupervised accumulated aggregation shifting for robust defect detection in real industry

Yaping Yan; Shun’ichi Kaneko

doi:10.1117/12.2521748

16 July 2019 Unsupervised accumulated aggregation shifting for robust defect detection in real industry

Yaping Yan, Shun’ichi Kaneko

Author Affiliations +

Proceedings Volume 11172, Fourteenth International Conference on Quality Control by Artificial Vision; 1117212 (2019) https://doi.org/10.1117/12.2521748
Event: Fourteenth International Conference on Quality Control by Artificial Vision, 2019, Mulhouse, France

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.

Citation Download Citation

Yaping Yan and Shun’ichi Kaneko "Unsupervised accumulated aggregation shifting for robust defect detection in real industry", Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 1117212 (16 July 2019); https://doi.org/10.1117/12.2521748

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KEYWORDS

Defect detection

Image segmentation

Image enhancement

Inspection

Statistical analysis

Machine vision

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