Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Crack Detection in Tunnel Using Convolutional Encoder-Decoder Network

컨볼루셔널 인코더-디코더 네트워크를 이용한 터널에서의 균열 검출

  • Han, Bok Gyu (Dept. Computer Science & Engineering., Hanyang University) ;
  • Yang, Hyeon Seok (Dept. Computer Science & Engineering., Hanyang University) ;
  • Lee, Jong Min (Dept. Computer Science & Engineering., Hanyang University) ;
  • Moon, Young Shik (Dept. Computer Science & Engineering., Hanyang University)
  • 한복규 (한양대학교 컴퓨터공학과) ;
  • 양현석 (한양대학교 컴퓨터공학과) ;
  • 이종민 (한양대학교 컴퓨터공학과) ;
  • 문영식 (한양대학교 컴퓨터공학과)
  • Received : 2017.01.23
  • Accepted : 2017.05.30
  • Published : 2017.06.25
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Abstract

The classical approaches to detect cracks are performed by experienced inspection professionals by annotating the crack patterns manually. Because of each inspector's personal subjective experience, it is hard to guarantee objectiveness. To solve this issue, automated crack detection methods have been proposed however the methods are sensitive to image noise. Depending on the quality of image obtained, the image noise affect overall performance. In this paper, we propose crack detection method using a convolutional encoder-decoder network to overcome these weaknesses. Performance of which is significantly improved in terms of the recall, precision rate and F-measure than the previous methods.

기존의 수작업으로 이루어지는 터널에서의 균열 검출은 점검자의 주관에 따라 균열을 판별하기 때문에 객관성을 보장하기 어렵다. 이러한 문제를 해결하기 위해서 터널에서 획득된 영상을 기반으로 균열을 검출하는 시스템이 많이 제안되었다. 하지만 기존의 방법은 터널 내부의 조명 상태, 균열 이외의 기타 에지 등 잡음에 상당히 민감하다. 이러한 단점은 터널의 상태에 따라 알고리즘의 성능을 크게 제한시킨다. 본 논문에서는 이러한 단점을 극복하기 위하여 컨볼루셔널 인코더-디코더 네트워크(Convolutional encoder-decoder network)를 이용한 균열 검출 방법을 제안한다. 제안하는 방법은 재현율과 정확률의 비교를 통하여 기존 연구에 비해 성능이 크게 향상되었음을 보였다.

Keywords

References

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