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Pixel-level Crack Detection in X-ray Computed Tomography Image of Granite using Deep Learning

딥러닝을 이용한 화강암 X-ray CT 영상에서의 균열 검출에 관한 연구

  • Hyun, Seokhwan (School of Civil and Environmental Engineering, Yonsei University) ;
  • Lee, Jun Sung (School of Civil and Environmental Engineering, Yonsei University) ;
  • Jeon, Seonghwan (School of Civil and Environmental Engineering, Yonsei University) ;
  • Kim, Yejin (School of Civil and Environmental Engineering, Yonsei University) ;
  • Kim, Kwang Yeom (Korea Institute of Civil Engineering and Building Technology) ;
  • Yun, Tae Sup (School of Civil and Environmental Engineering, Yonsei University)
  • 현석환 (연세대학교 건설환경공학과) ;
  • 이준성 (연세대학교 건설환경공학과) ;
  • 전성환 (연세대학교 건설환경공학과) ;
  • 김예진 (연세대학교 건설환경공학과) ;
  • 김광염 (건설기술연구원 극한환경연구센터) ;
  • 윤태섭 (연세대학교 건설환경공학과)
  • Received : 2019.06.05
  • Accepted : 2019.06.24
  • Published : 2019.06.30

Abstract

This study aims to extract a 3D image of micro-cracks generated by hydraulic fracturing tests, using the deep learning method and X-ray computed tomography images. The pixel-level cracks are difficult to be detected via conventional image processing methods, such as global thresholding, canny edge detection, and the region growing method. Thus, the convolutional neural network-based encoder-decoder network is adapted to extract and analyze the micro-crack quantitatively. The number of training data can be acquired by dividing, rotating, and flipping images and the optimum combination for the image augmentation method is verified. Application of the optimal image augmentation method shows enhanced performance for not only the validation dataset but also the test dataset. In addition, the influence of the original number of training data to the performance of the deep learning-based neural network is confirmed, and it leads to succeed the pixel-level crack detection.

Keywords

Granite;X-ray CT;Deep learning;Crack detection;Image augmentation

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Fig. 1. Example of the cross-sectional X-ray CT image of granite including micro-crack

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Fig. 2. Crack detection by conventional image processing method. (a) an original image and (b) a ground-truth image and (c) the extracted crack by region growing method and (d) the extracted crack by locally adaptive thresholding method

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Fig. 3. Overall architecture of the convolutional neural network based deep learning network for extracting micro-crack in X-ray CT image

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Fig. 4. Recall, precision, and f-measure from validation data (a) with image augmentation and (b) without image augmentation

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Fig. 5. Recall, precision, and f-measure from test data with respect to the number of images for training and validation

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Fig. 6. Examples of crack extraction results using CNN-based neural network (a) original image from test data and (b) ground-truth image and (c) extracted crack image with 30 training and validation images and (d) extracted crack image with 90 training and validation images

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Fig. 7. 3-D crack surface visualization (a) specimen including the extracted crack surface which propagated from a borehole. Extracted crack surface from (b) the front and (c) the side

Table 1. Crack detection performance by data augmentation using image division

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Table 2. Crack detection performance by data augmentation using image rotation on the original 30 images

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Table 3. Crack detection performance by data augmentation using image division and rotation

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Table 4. Crack detection performance by data augmentation using image flipping

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Acknowledgement

Grant : 지반 안정화를 위한 친환경 바이오공법 적용방안

Supported by : 한국연구재단(NRF), 토지주택연구원

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