KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Deep Learning Models for Autonomous Crack Detection System

자동화 균열 탐지 시스템을 위한 딥러닝 모델에 관한 연구

  • 지홍근 (성균관대학교 인공지능융합학과) ;
  • 김지나 (성균관대학교 인터랙션사이언스) ;
  • 황시정 (성균관대학교 인터랙션사이언스) ;
  • 김도건 (성균관대학교 인공지능융합학과) ;
  • 박은일 (성균관대학교 인터랙션사이언스학과, 인공지능융합학과) ;
  • 김영석 (한국건설기술연구원 인프라안전연구본부) ;
  • 류승기 (한국건설기술연구원 차세대 인프라연구센터)
  • Received : 2020.10.14
  • Accepted : 2020.11.14
  • Published : 2021.05.31
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Abstract

Cracks affect the robustness of infrastructures such as buildings, bridge, pavement, and pipelines. This paper presents an automated crack detection system which detect cracks in diverse surfaces. We first constructed the combined crack dataset, consists of multiple crack datasets in diverse domains presented in prior studies. Then, state-of-the-art deep learning models in computer vision tasks including VGG, ResNet, WideResNet, ResNeXt, DenseNet, and EfficientNet, were used to validate the performance of crack detection. We divided the combined dataset into train (80%) and test set (20%) to evaluate the employed models. DenseNet121 showed the highest accuracy at 96.20% with relatively low number of parameters compared to other models. Based on the validation procedures of the advanced deep learning models in crack detection task, we shed light on the cost-effective automated crack detection system which can be applied to different surfaces and structures with low computing resources.

균열은 건물, 교량, 도로, 수송관 등의 기반시설의 안전성에 영향을 주는 요소이다. 본 연구에서는 검사 비용과 시간을 줄일 수 있는 자동화된 균열 탐지 시스템을 다룬다. 환경과 표면에 강건한 시스템을 구성하기 위해서, 본 연구에서는 여러 사전 연구에서 사용된 다양한 표면의 균열 데이터 셋을 수집하여 통합 데이터 셋을 구축하였다. 이후, 컴퓨터 비전 분야에 높은 성능을 발휘하는 VGG, ResNet, WideResNet, ResNeXt, DenseNet, EfficientNet 딥러닝 모델을 적용하였다. 통합 데이터 셋은 훈련 집합(80%)과 테스트 집합(20%)으로 나누어 모델 성능을 검증하기 위해서 사용했다. 실험 결과, DenseNet121 모델이 높은 마라미터 효율성을 가지면서도 테스트 집합에 대해 96.20%의 정확도를 달성하여 가장 높은 성능을 보여주었다. 딥러닝 모델의 균열 검출 성능 검증을 통해, DenseNet121를 활용하여 컴퓨팅 자원이 적은 소형 디바이스에서도 높은 균열 검출 성능을 보이는 탐지 시스템을 구축이 가능함을 확인했다.

Keywords

Acknowledgement

이 논문은 한국건설기술연구원 주요사업(지하매설 압력관의 실시간 건전성 진단 및 관리 기술 개발)의 지원을 받아 수행된 연구결과이며 이에 감사드립니다.

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