Adversarial learning for underground structure concrete crack detection based on semi­supervised semantic segmentation

지하구조물 콘크리트 균열 탐지를 위한 semi-supervised 의미론적 분할 기반의 적대적 학습 기법 연구

  • Shim, Seungbo (Future Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology) ;
  • Choi, Sang-Il (Future Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology) ;
  • Kong, Suk-Min (Future Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology) ;
  • Lee, Seong-Won (Future Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology)
  • 심승보 (한국건설기술연구원 차세대 인프라연구센터) ;
  • 최상일 (한국건설기술연구원 차세대 인프라연구센터) ;
  • 공석민 (한국건설기술연구원 차세대 인프라연구센터) ;
  • 이성원 (한국건설기술연구원 차세대 인프라연구센터)
  • Received : 2020.07.21
  • Accepted : 2020.08.04
  • Published : 2020.09.30


Underground concrete structures are usually designed to be used for decades, but in recent years, many of them are nearing their original life expectancy. As a result, it is necessary to promptly inspect and repair the structure, since it can cause lost of fundamental functions and bring unexpected problems. Therefore, personnel-based inspections and repairs have been underway for maintenance of underground structures, but nowadays, objective inspection technologies have been actively developed through the fusion of deep learning and image process. In particular, various researches have been conducted on developing a concrete crack detection algorithm based on supervised learning. Most of these studies requires a large amount of image data, especially, label images. In order to secure those images, it takes a lot of time and labor in reality. To resolve this problem, we introduce a method to increase the accuracy of crack area detection, improved by 0.25% on average by applying adversarial learning in this paper. The adversarial learning consists of a segmentation neural network and a discriminator neural network, and it is an algorithm that improves recognition performance by generating a virtual label image in a competitive structure. In this study, an efficient deep neural network learning method was proposed using this method, and it is expected to be used for accurate crack detection in the future.


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