| Summary: | 碩士 === 朝陽科技大學 === 工業工程與管理系碩士班 === 99 === Transparent glass products have become necessities in our daily life and major materials for construction, optical and electronic industries. Since the surface defects directly affect the quality of the transparent glass products, the detection of surface defects is very important for manufacturers. Human inspection is easy to be interfered by the external object images reflected or transmitted on the surface of transparent glass and results in making erroneous judgments of defect detections. Moreover, the surface of transparent glass product is easily attached to dust, dirt, fingerprints, and so on and make the defect inspection tasks more difficult. Therefore, this research aims at exploring the automated inspection of surface defects of car transparent glasses.
This study develops an automated visual defect inspection system of car transparent glasses. We first take Hartley transform of a testing image to frequency domain. The entropies of different radiuses of frequency components are calculated in Hartley domain. The fuzzy inference algorithm combining fuzzy rules and fuzzy membership functions is applied to choose adequate sizing factors (shrink coefficient matrix) based on entropy changes for modifying frequency components. After the frequency components are modified, the frequency domain image is transferred back to the spatial domain and yields the desired enhanced image. Then, the visual defects with low intensity contrast can be easily detected. Experimental results show that the defect detection rates achieve up to 98.08% and the false alarm rates lower to 1.68% by the proposed method and outperform the traditional defect detection methods.
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