Affiliations: Department of Electronics and Information, Hangzhou DianZi University, Hangzhou, Zhejiang, China
Correspondence:
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Corresponding author: Yuxiang Yang, Department of Electronics and Information, Hangzhou DianZi University, Hangzhou, Zhejiang, China. E-mail:[email protected]
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.
