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2022 Vol.24, Issue 6 Preview Page

Research Paper

Deep learning based crack detection from tunnel cement concrete lining

딥러닝 기반 터널 콘크리트 라이닝 균열 탐지

Soohyeon Bae1
,
Sangwoo Ham2
,
Impyeong Lee3
,
Gyu-Phil Lee4
,
Donggyou Kim5*
배 수현1
,
함 상우2
,
이 임평3
,
이 규필4
,
김 동규5*
1Master Student, Dept. of Geoinformatics, University of Seoul
2Ph.D. Candidate, Dept. of Geoinformatics, University of Seoul
3Professor, Dept. of Geoinformatics, University of Seoul
4Senior Researcher, Dept. of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology
5Research Fellow, Dept. of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology
1비회원, 서울시립대학교 대학원 공간정보공학과 석사과정
2정회원, 서울시립대학교 대학원 공간정보공학과 박사과정
3비회원, 서울시립대학교 공간정보공학과 정교수
4정회원, 한국건설기술연구원 지반연구본부 수석연구원
5정회원, 한국건설기술연구원 지반연구본부 연구위원
*Corresponding Author
30 November 2022. pp. 583-598
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Abstract

As human-based tunnel inspections are affected by the subjective judgment of the inspector, making continuous history management difficult. There is a lot of deep learning-based automatic crack detection research recently. However, the large public crack datasets used in most studies differ significantly from those in tunnels. Also, additional work is required to build sophisticated crack labels in current tunnel evaluation. Therefore, we present a method to improve crack detection performance by inputting existing datasets into a deep learning model. We evaluate and compare the performance of deep learning models trained by combining existing tunnel datasets, high-quality tunnel datasets, and public crack datasets. As a result, DeepLabv3+ with Cross-Entropy loss function performed best when trained on both public datasets, patchwise classification, and oversampled tunnel datasets. In the future, we expect to contribute to establishing a plan to efficiently utilize the tunnel image acquisition system’s data for deep learning model learning.

Keywords
Deep learning
Crack detection
Semantic segmentation
Image processing
Concrete lining

인력기반 터널 점검은 점검자의 주관적인 판단에 영향을 받으며 지속적인 이력관리가 어렵다. 따라서 최근에는 딥러닝 기반 자동 균열 탐지 연구가 활발히 진행되고 있다. 하지만 대부분의 연구에서는 사용하는 대규모 공개 균열 데이터셋은 터널 내부에서 발생하는 균열과 매우 상이하다. 또한 현행 터널 상태평가에서 정교한 균열 레이블을 구축하기 위해서는 추가적인 작업이 요구된다. 이에 본 연구는 균열 형상이 다소 단순하게 표현된 기존 데이터셋을 딥러닝 모델에 입력하여 균열 탐지 성능을 개선하는 방안을 제시한다. 기존 터널 데이터셋, 고품질 터널 데이터셋과 공개 균열 데이터셋을 조합하여 학습한 딥러닝 모델의 성능 평가와 비교를 수행한다. 그 결과 Cross Entropy 손실함수를 사용한 DeepLabv3+에 공개 데이터셋, 패치 단위 분류와 오버샘플링을 수행한 터널 데이터셋을 모두 학습한 경우 성능이 가장 좋았다. 향후 기 구축된 터널 영상 취득 시스템 데이터를 딥러닝 모델 학습에 효율적으로 활용하기 위한 방안을 수립하는 데 기여할 것으로 기대한다.

키워드
딥러닝
균열탐지
의미론적 분할
영상처리
콘크리트 라이닝
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Information
  • Publisher :Korean Tunneling and Underground Space Association
  • Publisher(Ko) :한국터널지하공간학회
  • Journal Title :Journal of Korean Tunnelling and Underground Space Association
  • Journal Title(Ko) :한국터널지하공간학회 논문집
  • Volume : 24
  • No :6
  • Pages :583-598
  • Received Date : 2022-09-27
  • Revised Date : 2022-10-20
  • Accepted Date : 2022-10-20
  • DOI :https://doi.org/10.9711/KTAJ.2022.24.6.583
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