Fig. 1. Example of the cross-sectional X-ray CT image of granite including micro-crack
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
Fig. 3. Overall architecture of the convolutional neural network based deep learning network for extracting micro-crack in X-ray CT image
Fig. 4. Recall, precision, and f-measure from validation data (a) with image augmentation and (b) without image augmentation
Fig. 5. Recall, precision, and f-measure from test data with respect to the number of images for training and validation
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
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
Table 2. Crack detection performance by data augmentation using image rotation on the original 30 images
Table 3. Crack detection performance by data augmentation using image division and rotation
Table 4. Crack detection performance by data augmentation using image flipping
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