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http://dx.doi.org/10.7474/TUS.2019.29.3.184

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

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)
Publication Information
Tunnel and Underground Space / v.29, no.3, 2019 , pp. 184-196 More about this Journal
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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