Journal of the Korean Society of Safety (한국안전학회지)

The Korean Society of Safety (한국안전학회)
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A Comparative Study on Performance of Deep Learning Models for Vision-based Concrete Crack Detection according to Model Types

영상기반 콘크리트 균열 탐지 딥러닝 모델의 유형별 성능 비교

  • Kim, Byunghyun (Department of Civil Engineering, University of Seoul)) ;
  • Kim, Geonsoon (Institute for Industrial Technology, University of Seoul) ;
  • Jin, Soomin (Department of Civil Engineering, University of Seoul)) ;
  • Cho, Soojin (Department of Civil Engineering, University of Seoul))
  • 김병현 (서울시립대학교 토목공학과) ;
  • 김건순 (서울시립대학교 산업기술연구소) ;
  • 진수민 (서울시립대학교 토목공학과) ;
  • 조수진 (서울시립대학교 토목공학과)
  • Received : 2019.11.27
  • Accepted : 2019.12.12
  • Published : 2019.12.31
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Abstract

In this study, various types of deep learning models that have been proposed recently are classified according to data input / output types and analyzed to find the deep learning model suitable for constructing a crack detection model. First the deep learning models are classified into image classification model, object segmentation model, object detection model, and instance segmentation model. ResNet-101, DeepLab V2, Faster R-CNN, and Mask R-CNN were selected as representative deep learning model of each type. For the comparison, ResNet-101 was implemented for all the types of deep learning model as a backbone network which serves as a main feature extractor. The four types of deep learning models were trained with 500 crack images taken from real concrete structures and collected from the Internet. The four types of deep learning models showed high accuracy above 94% during the training. Comparative evaluation was conducted using 40 images taken from real concrete structures. The performance of each type of deep learning model was measured using precision and recall. In the experimental result, Mask R-CNN, an instance segmentation deep learning model showed the highest precision and recall on crack detection. Qualitative analysis also shows that Mask R-CNN could detect crack shapes most similarly to the real crack shapes.

Keywords

References

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