Browse > Article
http://dx.doi.org/10.7474/TUS.2019.29.6.508

A Study on Automatic Classification of Characterized Ground Regions on Slopes by a Deep Learning based Image Segmentation  

Lee, Kyu Beom (University of Science and Technology & Korea Institute of Civil Engineering and Building Technology)
Shin, Hyu-Soung (Korea Institute of Civil Engineering and Building Technology)
Kim, Seung Hyeon (Korea Institute of Civil Engineering and Building Technology)
Ha, Dae Mok (Seoul National University of Science and Technology)
Choi, Isu (Hanyang University)
Publication Information
Tunnel and Underground Space / v.29, no.6, 2019 , pp. 508-522 More about this Journal
Abstract
Because of the slope failure, not only property damage but also human damage can occur, slope stability analysis should be conducted to predict and reinforce of the slope. This paper, defines the ground areas that can be characterized in terms of slope failure such as Rockmass jointset, Rockmass fault, Soil, Leakage water and Crush zone in sloped images. As a result, it was shown that the deep learning instance segmentation network can be used to recognize and automatically segment the precise shape of the ground region with different characteristics shown in the image. It showed the possibility of supporting the slope mapping work and automatically calculating the ground characteristics information of slopes necessary for decision making such as slope reinforcement.
Keywords
Instance segmentation; Deep learning; Slope failure image; Ground characteristics of Slope;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Bourdev, J., Girshick , R., Dollar, P., and Zitnick, C. L., 2014, Microsoft coco: Common objects in context, In European conference on computer vision, pp. 740-755.
2 Ministry of Land, Transport and Maritime Affairs(MLTMA), 2011, 도로비탈면 유지관리 실무매뉴얼.
3 Nair, V., and Hinton, G. E., 2010, Rectified linear units improve restricted boltzmann machines, In Proceedings of the 27th international conference on machine learning (ICML-10), pp. 807-814.
4 Park, D. G., Kim, T. H., and Park, J. H., 2006, 우리나라 비탈면재해 피해현황과 대책, Geotechnical Engineering, Vol. 22, No. 6, pp. 6-18.
5 Ren, S., He, K., Girshick, R., and Sun, J., 2015, Faster r-cnn: Towards real-time object detection with region proposal networks, In Advances in neural information processing systems, pp. 91-99.
6 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R., 2014, Dropout: a simple way to prevent neural networks from overfitting, The journal of machine learning research, Vol. 15, No. 1, pp. 1929-1958.
7 Voulodimos, A., Doulamis, N., Doulamis, A., and Protopapadakis, E., 2018, Deep learning for computer vision: A brief review, Computational intelligence and neuroscience.
8 Zhao, H., 2018, New image processing algorithm for geological structure identification of rock slopes based on drone-captured images, PhD Thesis, Colorado School of Mines, Arthur Lakes Library.
9 Zhu, M., 2004, Recall, precision and average precision, Department of Statistics and Actuarial Science, University of Waterloo.
10 Zitnick, C. L., and Dollar, P., 2014, Edge boxes: Locating object proposals from edges, In European conference on computer vision, pp. 391-405.
11 Kim, J., T., Lee, C., J., Kim, J., H., Lee, Y., S., Ahn, K., H., Kim, S., D., and Jeong, G., C., 2008, Mapping and Slope Stability Analysis in Weathered Gneiss, Conference of Korean Society of Engineering Geology, pp. 163-170.
12 Brooks, J., 2019, COCO Annotator, https://github.com/jsbroks/coco-annotator/
13 He, K., Gkioxari, G., Dollar, P., and Girshick, R., 2017, Mask r-cnn, In Proceedings of the IEEE international conference on computer vision, pp. 2961-2969.
14 He, K., Zhang, X., Ren, S., and Sun, J., 2016, Deep residual learning for image recognition, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778.
15 Ioffe, S., and Szegedy, C., 2015, Batch normalization: Accelerating deep network training by reducing internal covariate shift, arXiv preprint arXiv:1502.03167.
16 Kim, J., H., and Kim, J., D., 2001, 암반 비탈면 영상을 이용한 절리의 방향성 해석, 한국암반공학회 학술대회 및 세미나 자료집, pp. 59-68.
17 Korea Institute of Civil Engineering and Building Technology(KICT), 2016, 옥동-농소 2공구 도로개설공사: 비탈면 정밀조사 및 대책안 제시 검토의견서.
18 Lim, C.U., 2017, Mask R-CNN, www.slideshare.net/windmdk/mask-rcnn
19 Krizhevsky, A., Sutskever, I., and Hinton, G. E., 2012, Imagenet classification with deep convolutional neural networks, In Advances in neural information processing systems, pp. 1097-1105.
20 LeCun, Y., Bengio, Y., and Hinton, G., 2015, Deep learning, Nature, Vol. 521, pp. 436-444.   DOI