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A Study on the Defect Classification of Low-contrast·Uneven·Featureless Surface Using Wavelet Transform and Support Vector Machine  

Kim, Sung Joo (Graduate school, Korea National University of Transportation)
Kim, Gyung Bum (Aeronautical & Mechanical Design Engineering, Korea National University of Transportation)
Publication Information
Journal of the Semiconductor & Display Technology / v.19, no.3, 2020 , pp. 1-6 More about this Journal
Abstract
In this paper, a method for improving the defect classification performance in steel plate surface has been studied, based on DWT(discrete wavelet transform) and SVM(support vector machine). Surface images of the steel plate have low contrast, uneven, and featureless, so that the contrast between defect and defect-free regions is not discriminated. These characteristics make it difficult to extract the feature of the surface defect image. In order to improve the characteristics of these images, a synthetic images based on discrete wavelet transform are modeled. Using the synthetic images, edge-based features are extracted and also geometrical features are computed. SVM was configured in order to classify defect images using extracted features. As results of the experiment, the support vector machine based classifier showed good classification performance of 94.3%. The proposed classifier is expected to contribute to the key element of inspection process in smart factory.
Keywords
Defect Classification; Discrete Wavelet Transform(DWT); Surface Defect; Support Vector Machine;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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