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Proceedings Paper

A defect detection algorithm based on statistical feature of local visual field for complex metal curve surface
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Paper Abstract

In view of the difficulty of defects detection of complex metal curve surface in uneven illumination and high speed processing, a new, simple, yet robust algorithm based on statistical feature of local visual field is proposed. This algorithm first performs the ideal image difference by extracting the template from the image itself, and then computes the statistical feature in local visual field to correct the gray-scale fluctuation in each region of image. In this way, the influence of the uneven illumination at low and high frequency is eliminated concurrently, which achieves the equalization of the statistical features of the local visual fields except the position containing the defect, so as to use the global threshold in whole image reasonably; Next, on the search of defects, this paper replaces the pixel level with the local field of vision and compresses the image information with the defects’ scale which is in line with the human eye. This not only reduces the influence of random noise, but also greatly improves the processing speed while preserving defects information, which makes it possible to realize real-time processing ability for image with the large amount of data. To detect complex curved surface on semi-finished metal shell of cell phone, the experimental results demonstrate that the defects detection accuracy of the proposed algorithm can reach 95%, and the detection time for single test area is less than 1ms, which is suitable for accurate and real-time detection on the production line for such surface defect.

Paper Details

Date Published: 24 July 2018
PDF: 6 pages
Proc. SPIE 10827, Sixth International Conference on Optical and Photonic Engineering (icOPEN 2018), 108271B (24 July 2018); doi: 10.1117/12.2500384
Show Author Affiliations
Rongzhi Liu, Zhejiang Univ. (China)
Yongying Yang, Zhejiang Univ. (China)
Chen Li, Zhejiang Univ. (China)
Fanyi Wang, Zhejiang Univ. (China)
Yubin Du, Zhejiang Univ. (China)
Xiang Xiao, Zhejiang Univ. (China)
Guohua Feng, Zhejiang Univ. (China)
Yanwei Li, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 10827:
Sixth International Conference on Optical and Photonic Engineering (icOPEN 2018)
Yingjie Yu; Chao Zuo; Kemao Qian, Editor(s)

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