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2018 Vol.34, Issue 12 Preview Page

Research Article

Crack Detection of Concrete Structure Using Deep Learning and Image Processing Method in Geotechnical Engineering

딥러닝과 영상처리기법을 이용한 콘크리트 지반 구조물 균열 탐지

Ah-Ram Kim1
,
Donghyeon Kim2
,
Yo-Seph Byun3
,
Seong-Won Lee4*
김 아람1
,
김 동현2
,
변 요셉3
,
이 성원4*
1Member, Ph.D., Dept. of Infrastructure Safety Research, KICT
2Ph.D., NEROPHET Inc.
3Senior Researcher, Dept. of Infrastructure Safety Research, KICT
4Member, Senior Research Fellow, Dept. of Infrastructure Safety Research, KICT
1정회원, 한국건설기술연구원 인프라안전연구본부 공학박사
2비회원, 뉴로핏 주식회사 공학박사
3비회원, 한국건설기술연구원 인프라안전연구본부 수석연구원
4정회원, 한국건설기술연구원 인프라안전연구본부 선임연구위원
* Corresponding Author
31 December 2018. pp. 145-154
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Abstract

The damage investigation and inspection methods performed in concrete facilities such as bridges, tunnels, retaining walls and so on, are usually visually examined by the inspector using the surveying tool in the field. These methods highly depend on the subjectivity of the inspector, which may reduce the objectivity and reliability of the record. Therefore, the new image processing techniques are necessary in order to automatically detect the cracks and objectively analyze the characteristics of cracks. In this study, deep learning and image processing technique were developed to detect cracks and analyze characteristics in images for concrete facilities. Two-stage image processing pipeline was proposed to obtain crack segmentation and its characteristics. The performance of the method was tested using various crack images with a label and the results showed over 90% of accuracy on crack classification and segmentation. Finally, the crack characteristics (length and thickness) of the crack image pictured from the field were analyzed, and the performance of the developed technique was verified by comparing the actual measured values and errors.

Keywords
Concrete structure
Crack detection
Deep learning
Image processing method

교량, 터널 옹벽 등의 콘크리트 구조물에서 수행되는 손상 조사 및 검사 방법은 일반적으로 검사원이 현장에서 직접 측량 도구를 사용하여 시각적으로 검사하는 방법이다. 이 방법은 검사원의 주관성에 크게 의존하기 때문에 기록의 객관성과 신뢰성이 떨어지게 된다. 따라서 균열을 자동으로 탐지하고 균열 특성을 객관적으로 분석할 수 있는 새로운 이미지분석기법이 필요하다. 본 연구에서는 콘크리트 이미지에서 균열을 검출하고 특성(균열의 길이, 폭)을 분석하기 위한 딥러닝 및 이미지분석기법을 개발하였다. 균열 검출과 해당 균열의 특성을 얻기 위해 두 가지 단계의 방법이 제안되었다. 제안된 방법의 성능을 검증하기 위하여 라벨이 있는 다양한 균열 이미지가 사용되었으며, 균열 판단과 구획화에 대해 90% 이상의 정확도를 확인하였다. 최종적으로 실제 촬영된 균열 영상의 균열 특성을 분석하고 실제 측정치와 오차를 확인하여 개발된 기법의 성능을 검증하였다.

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Information
  • Publisher :The Korean Geotechnical Society
  • Publisher(Ko) :한국지반공학회
  • Journal Title :Journal of the Korean Geotechnical Society
  • Journal Title(Ko) :한국지반공학회 논문집
  • Volume : 34
  • No :12
  • Pages :145-154
  • Received Date : 2018-12-14
  • Revised Date : 2018-12-27
  • Accepted Date : 2018-12-27
  • DOI :https://doi.org/10.7843/kgs.2018.34.12.145
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