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Subtitle:
Article type: Research Article
Authors: Yang, Yuxiang* | Gao, Mingyu | Yin, Ke | Wu, Zhanxiong | Li, Yun
Affiliations: Department of Electronics and Information, Hangzhou DianZi University, Hangzhou, Zhejiang, China
Correspondence: [*] Corresponding author: Yuxiang Yang, Department of Electronics and Information, Hangzhou DianZi University, Hangzhou, Zhejiang, China. E-mail:yyx@hdu.edu.cn
Abstract: Limited to the impact of human visual sensitivity and individual subjective judgments, traditionally manual detection method is unable to accurately and reliably capture the cone surface defects, and will easily cause a lot of missing and false detection. In this paper, we propose a novel machine vision system for cone surface defect inspection. Specifically, we apply gradient and regional consistency detection algorithms to check the surface defects. And then the detection results will be sent via the serial port to the mechanical separation system, and the defective cones will be peeled off the production line by motor. The proposed system can improve the defect detection efficiency and product quality, also can reduce production costs. Experimental results demonstrate that the proposed approach can obtain superior performance over traditional manual method and has high detection efficiency.
Keywords: Cone defect, machine vision, gradient detection, regional consistency
DOI: 10.3233/JCM-150541
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 15, no. 2, pp. 269-276, 2015
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