한국터널지하공간학회 논문집 (Journal of Korean Tunnelling and Underground Space Association)

  • 제24권6호
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  • Pages.583-598
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  • 2022
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  • 2233-8292(pISSN)
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  • 2287-4747(eISSN)
한국터널지하공간학회 (Korean Tunnelling and Underground Space Association)
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딥러닝 기반 터널 콘크리트 라이닝 균열 탐지

Deep learning based crack detection from tunnel cement concrete lining

  • 배수현 (서울시립대학교 대학원 공간정보공학과) ;
  • 함상우 (서울시립대학교 대학원 공간정보공학과) ;
  • 이임평 (서울시립대학교 공간정보공학과) ;
  • 이규필 (한국건설기술연구원 지반연구본부) ;
  • 김동규 (한국건설기술연구원 지반연구본부)
  • Bae, Soohyeon (Dept. of Geoinformatics, University of Seoul) ;
  • Ham, Sangwoo (Dept. of Geoinformatics, University of Seoul) ;
  • Lee, Impyeong (Dept. of Geoinformatics, University of Seoul) ;
  • Lee, Gyu-Phil (Dept. of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Kim, Donggyou (Dept. of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
  • 투고 : 2022.09.27
  • 심사 : 2022.10.20
  • 발행 : 2022.11.30
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초록

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.

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.

키워드

과제정보

본 논문은 한국건설기술연구원 주요사업으로 지원을 받아 수행된 연구(인공지능을 활용한 대심도 지하 대공간의 스마트 복합 솔루션 개발)로 이에 감사합니다.

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