Abstract
In the process of construction and operation of mountain roads, slope disasters such as landslide and collapse are often encountered, which seriously affect the transportation infrastructure and safe operation in China. Cracks are the early symptoms of most slope diseases. By monitoring the change trend of cracks, the displacement trajectory of the slope body can be reflected in time, which is of great significance for landslide monitoring and early warning, so the safety detection is concentrated in this stage. In recent years, great progress has been made in deep learning-based computer vision methods, which have the advantages of simple observation method, low cost, wide detection area and sustainable monitoring. In view of this, a pixel level segmentation method of slope cracks based on deep convolutional neural network is proposed in this paper. According to the shape characteristics of slope cracks, a deep convolutional neural network was designed. The network was trained on the self-made slope image data set, and the IOU on the validation set reached 75.26%, which realized the precise segmentation and recognition of cracks. Experimental results show that the model has a good ability to characterize the slope cracks, can accurately extract the slope cracks, and provides a reliable basis for the formulation of slope early warning and disaster relief programs.
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References
Fang, W., Zhang, F., Ding, Y., Sheng, J.: A new sequential image prediction method based on LSTM and DCGAN. Comput. Mater. Con. 64(1), 217–231 (2020)
Wang, X.: Design and implementation of landslide concentrator based on multi-sensor information fusion. Southwest Jiaotong University (2019)
Zeyuan, X.: The application of 3D laser scanning technology in slope monitoring. Surv. Spatial Geogr. Inf. 43(06), 222–224 (2020)
Xue, J., Li, X., Dong, L.: The clustering unascertained comprehensive identification method of slope stability and its application. Rock Soil Mech. (SI), 293–297 (2010)
Mohammad, I., Huang, H.: Monitoring fatigue crack growth and opening using antenna sensors. Smart Mater. Struct. 19, 055023-1–055023-8 (2010)
Uhlemann, S., et al.: Assessment of ground-based monitoring techniques applied to landslide investigations. Geomorphology 253, 438–451 (2016)
**ng, Z.: The application of three-dimensional laser scanning technology in slope monitoring. Surv. Spatial Geogr. Inf. 43(06), 222–224 (2020)
Fang, W., Pang, L., Yi, W.N.: Survey on the application of deep reinforcement learning in image processing. J. Artif. Intell. 2(1), 39–58 (2020)
Gu, B., **ong, W., Bai, Z.: Human action recognition based on supervised class-specific dictionary learning with deep convolutional neural network features. Comput. Mater. Con. 63(1), 243–262 (2020)
Liu, Y., Gu, X., Huang, L., Ouyang, J., Liao, M., Wu, L.: Analyzing periodicity and saliency for adult video detection. Multimedia Tools Appl. 79(7–8), 4729–4745 (2020)
Shin, H.K., Ahn, Y.H., Lee, S.H., Kim, H.Y.: Digital vision based concrete compressive strength evaluating model using deep convolutional neural network. Comput. Mater. Con. 61(3), 911–928 (2019)
Gibb, S., La, H.M., Louis, S.: A genetic algorithm for convolutional network structure optimization for concrete crack detection. In: 2018 IEEE Congress on Evolutionnary Computation (CEC), Piscataway, pp. 1–8. IEEE (2018)
Yamaguchi, T., Nakamura, S., Saegusa, R., Hashimoto, S.: Image-based crack detection for real concrete surfaces. IEEJ Trans. Electr. Electron. Eng. 3(1), 128–135 (2008)
Nishiyama, S., Minakata, N., Kikuchi, T., Yano, T.: Improved digital photogrammetry technique for crack monitoring. Adv. Eng. Inform. 29(4), 861 (2015)
Han, X., Zhao, Z.: Research on surface crack detection method based on computer vision technology. J. Build. Struct. 39(S1), 418–427 (2018)
Wang, N., Zhao, X., Zou, Z., et al.: Autonomous damage segmentation and measurement of glazed tiles in historic buildings via deep learning. Comput. Aided Civil Infrastruct. Eng. 118, 103291 (2020)
Ji, A., Xue, X., Wang, Y., Luo, X., Xue, W.: An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement. Autom. Constr. 114, 103176 (2020)
Kang, D., Benipal, S.S., Gopal, D.L., Cha, Y.-J.: Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning. Autom. Constr. 118, 103291 (2020)
Wei, T., Faning, D., Yanwen, L., et al.: Application of support vector machine in concrete CT image analysis. J. Hydraul. Eng. 7, 889–894 (2008)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Qayyum, A., Ahmad, I., Iftikhar, M., Mazher, M.: Object detection and fuzzy-based classification using UAV data. Intell. Autom. Soft Comput. 26(4), 693–702 (2020)
Acknowledgement
This thesis was completed under the careful guidance of my tutor Professor Liu Pengyu. My tutor’s profound professional knowledge, rigorous academic attitude and excellent work style had a profound influence on me, which not only enabled me to set a lofty academic goal, but also enabled me to master the truth of dealing with people. Here, I would like to express my high respect and heartfelt thanks to my tutor. Through the writing of this paper, I can systematically and comprehensively master the knowledge related to slope disaster monitoring and in-depth learning, and learn from the valuable experience of many scholars, which is a rare treasure for me. Due to the limited level of this theory, some of the points in the paper are inevitably inadequate, teachers and experts are welcome to criticize and correct.
Funding
This paper is supported by the following funds: National Key R&D Program of China (2018YFF01010100), National natural science foundation of China (61672064), Basic Research Program of Qinghai Province under Grants No. 2021-ZJ-704 and Advanced information network Bei**g laboratory (PXM2019_014204_5000 29).
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Li, Y., Liu, P., Chen, S., Jia, K., Liu, T. (2021). The Identification of Slope Crack Based on Convolutional Neural Network. In: Sun, X., Zhang, X., **a, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1423. Springer, Cham. https://doi.org/10.1007/978-3-030-78618-2_2
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