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Image-based segmentation and quantification of weak interlayers in rock tunnel face via deep learning

Chen, J ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1016/j.autcon.2020.103371
  3. Publisher: Elsevier B.V , 2020
  4. Abstract:
  5. In this paper, an advanced integrated pixel-level method based on the deep convolutional neural network (DCNN) approach named DeepLabv3+ is proposed for weak interlayers detection and quantification. Furthermore, a database containing 32,040 images of limestone, dolomite, loess clay, and red clay is established to verify this method. The proposed model is then trained, validated, and tested via feeding multiple weak interlayers. Moreover, robustness and adaptability of the proposed model are evaluated, and the weak interlayers are extracted. Compared with the fully convolutional network (FCN)-based method and traditional image techniques, the proposed model provides higher accuracy in terms of boundary recognition. Besides, it can further detect multiple weak interlayers at the pixel level in practice. The results reveal that the proposed model can efficiently segment damage for rock tunnel faces, eliminate more noises, and consequently provide a much faster running speed. © 2020 Elsevier B.V
  6. Keywords:
  7. Convolutional neural network ; Image segmentation ; Rock tunnel ; Weak interlayer ; Convolution ; Convolutional neural networks ; Deep neural networks ; Lime ; Pixels ; Boundary recognition ; Convolutional networks ; Detection and quantifications ; Image-based ; Pixel level ; Running speed ; Weak interlayers ; Deep learning
  8. Source: Automation in Construction ; Volume 120 , 2020
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0926580520309511