| Summary: | 碩士 === 國立臺北科技大學 === 自動化科技研究所 === 97 === This paper develops a system of automatic optical inspection for surface defects of PVC card using machine vision and digital image processing. During the manufacturing process of PVC cards, the defects are generated because of the painting machine and artificial neglect. The types of defect are inaccurate size, scratches, spots, bubbles and pollution. Most of the manufactures still depend on human vision for inspecting. Inspection rate and result not only low, inaccuracy and error-prone, but also does not maintain a stable inspection quality. In this study, we develop automatic optical inspection techniques in order to replace human vision and increase the inspection speed, improve the inspection quality.
Because of the high reflecting surface of the PVC cards, so this paper applies dark-field imaging technique to solve the reflect problem. And inspect PVC cards size by using back lighting and Hough lines detection. In order to positioning and detecting a pattern, we using moment with resolution pyramid search method to match characteristics of the pattern. Eigenvalues, major-axis angle and Correlation coefficient of the covariance matrix of the data points in the map are used as similarity measures to evaluate the difference between two compared images. And uses Back-propagation neural network(BP) to recognize kind of the defects. Experimental results have shown that the proposed method can effectively detect defects. The average accuracy of inspection is about 92%. So using this inspection system can improve faults of human vision.
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