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

웨이블렛변환과 서포트벡터머신을 이용한 저대비·불균일·무특징 표면 결함 분류에 관한 연구

  • Kim, Sung Joo (Graduate school, Korea National University of Transportation) ;
  • Kim, Gyung Bum (Aeronautical & Mechanical Design Engineering, Korea National University of Transportation)
  • 김성주 (한국교통대학교 대학원) ;
  • 김경범 (한국교통대학교 기계자동차항공공학부)
  • Received : 2020.05.01
  • Accepted : 2020.08.27
  • Published : 2020.09.30

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.

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