An automatic visual inspection system for cone surface defects

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.