A Study on the Classification of Surface Defect Based on Deep Convolution Network and Transfer-learning

신경망과 전이학습 기반 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 : 2021.03.09
  • Accepted : 2021.03.18
  • Published : 2021.03.31

Abstract

In this paper, a method for improving the defect classification performance in low contrast, ununiformity and featureless steel plate surfaces has been studied based on deep convolution neural network and transfer-learning neural network. The steel plate surface images have low contrast, ununiformity, and featureless, so that the contrast between defect and defect-free regions are not discriminated. These characteristics make it difficult to extract the feature of the surface defect image. A classifier based on a deep convolution neural network is constructed to extract features automatically for effective classification of images with these characteristics. As results of the experiment, AlexNet-based transfer-learning classifier showed excellent classification performance of 99.43% with less than 160 seconds of training time. The proposed classification system showed excellent classification performance for low contrast, ununiformity, and featureless surface images.

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