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Volume 46 Issue 3
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LI Yuanhai, XU Xiaohua, ZHU Honghu, YANG Shuo, TANG Xiaojie, ZHAO Wanyong. Identification and characterization of rock fractures based on computer vision and software development[J]. Chinese Journal of Geotechnical Engineering, 2024, 46(3): 459-469. DOI: 10.11779/CJGE20221239
Citation: LI Yuanhai, XU Xiaohua, ZHU Honghu, YANG Shuo, TANG Xiaojie, ZHAO Wanyong. Identification and characterization of rock fractures based on computer vision and software development[J]. Chinese Journal of Geotechnical Engineering, 2024, 46(3): 459-469. DOI: 10.11779/CJGE20221239
shu

Identification and characterization of rock fractures based on computer vision and software development

  • 1.

    State Key Laboratory of Intelligent Construction and Healthy Operation & Maintenance of Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116, China

  • 2.

    School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China

  • 3.

    School of Earth Science and Engineering, Nanjing University, Nanjing 210023, China

  • 4.

    School of Civil Engineering, Xuzhou University of Technology, Xuzhou 221018, China

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  • Received Date: October 09, 2022
  • Available Online: March 14, 2024
    Graphical Abstract
    • Abstract
  • The characteristics of rock fractures are the core indices for evaluating the structure and integrity of rock masses, and are also the important factors for evaluating the safety and stability of rock engineering. For the rock fracture recognition based on deep learning, the Unet model is improved by introducing the convolutional block attention module, effectively improving the accuracy of rock fracture recognition. For separation and feature extraction of intersection fractures, a fracture separation and characterization algorithm based on the trace direction judgment is proposed. According to the characteristics of fracture separation, the overlapping tracing method or fracture tracing method is used to separate the intersection fracture skeleton. Then, the differential accumulation method, box method and linear regression method are employed to calculate the geometric characteristic indices such as the length, width and dip angle of the fractures. Based on the proposed algorithm, a set of software system for the intelligent identification and characterization of rock fracture images with a graphical user interface is developed, achieving complete functions from deep learning model parameter selection, model training, fracture recognition, quantitative analysis to result visualization. Finally, the performance of rock fracture recognition and separation characterization algorithms is evaluated. The results show that the improved Unet model has the best recognition performance for complex distribution fractures, and its overall recognition performance is superior to that of other networks. The skeleton separation algorithm achieves the expected results for the separation of common types of intersection fractures, and the characterization algorithm has high accuracy in characterizing the separation and intersection fractures. It has good application effects on actual rock fracture images. The research can provide reference for rock engineering tests and detection of rock mass structure by using the computer vision.
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    • References (14)
  • [1]
    张紫杉, 王述红, 王鹏宇, 等. 岩坡坡面裂隙网络智能识别与参数提取[J]. 岩土工程学报, 2021, 43(12): 2240-2248. doi: 10.11779/CJGE202112010

    ZHANG Zishan, WANG Shuhong, WANG Pengyu, et al. Intelligent identification and extraction of geometric parameters for surface fracture networks of rocky slopes[J]. Chinese Journal of Geotechnical Engineering, 2021, 43(12): 2240-2248. (in Chinese) doi: 10.11779/CJGE202112010
    [2]
    李术才, 刘洪亮, 李利平, 等. 基于数码图像的掌子面岩体结构量化表征方法及工程应用[J]. 岩石力学与工程学报, 2017, 36(1): 1-9. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201701001.htm

    LIU Shucai, LIU Hongliang, LI Liping, et al. A quantitative method for rock structure at working faces of tunnels based on digital images and its application[J]. Chinese Journal of Rock Mechanics and Engineering, 2017, 36(1): 1-9. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201701001.htm
    [3]
    LI Y H, TANG X J, ZHU H H. Optimization of the digital image correlation method for deformation measurement of geomaterials[J]. Acta Geotechnica, 2022, 17(12): 5721-5737. doi: 10.1007/s11440-022-01646-x
    [4]
    许文涛, 李晓昭, 章杨松, 等. 基于摄影测量系统的岩体结构面精细识别表征及应用[J]. 测绘学报, 2022, 51(10): 2093-2106. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202210009.htm

    XU Wentao, LI Xiaozhao, ZHANG Yangsong, et al. Fine identification and characterization of rock mass discontinuities and its application using a digital photogrammetry system[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10): 2093-2106. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202210009.htm
    [5]
    LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. doi: 10.1038/nature14539
    [6]
    LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, 2015: 3431-3440.
    [7]
    黄宏伟, 李庆桐. 基于深度学习的盾构隧道渗漏水病害图像识别[J]. 岩石力学与工程学报, 2017, 36(12): 2861-2871. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201712001.htm

    HUANG Hongwei, LI Qintong. Image recognition for water leakage in shield tunnel based on deep learning[J]. Chinese Journal of Rock Mechanics and Engineering, 2017, 36(12): 2861-2871. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201712001.htm
    [8]
    ZHU H, AZARAFZA M, AKGUN H. Deep learning-based key-block classification framework for discontinuous rock slopes[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2022, 14: 1131-1139. doi: 10.1016/j.jrmge.2022.06.007
    [9]
    薛东杰, 唐麒淳, 王傲, 等. 基于FCN的岩石混凝土裂隙几何智能识别[J]. 岩石力学与工程学报, 2019, 38(增刊2): 3393-3403. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2019S2014.htm

    XUE Dongjie, TANG Qichun, WANG Ao, et al. FCN-based intelligent identification of crack geometry in rock or concrete[J]. Chinese Journal of Rock Mechanics and Engineering, 2019, 38(S2): 3393-3403. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2019S2014.htm
    [10]
    刘金杉, 李元海, 卢昱杰, 等. 基于深度学习的隧道衬砌表观病害模拟检测系统[J]. 哈尔滨工业大学学报, 2022, 54(5): 24-33.

    LIU Jinshan, LI Yuanhai, LU Yujie, et al. Tunnel lining surface defect simulation and detection system based on deep learning[J]. Journal of Harbin Institute of Technology, 2022, 54(5): 24-33. (in Chinese)
    [11]
    SONG Q, WU Y, XIN X, et al. Real-time tunnel crack analysis system via deep learning[J]. IEEE Access, 2019, 7: 64186-64197. doi: 10.1109/ACCESS.2019.2916330
    [12]
    XIANG X, ZHANG Y, EL SADDIK A. Pavement crack detection network based on pyramid structure and attention mechanism[J]. IET Image Processing, 2020, 14(8): 1580-1586. doi: 10.1049/iet-ipr.2019.0973
    [13]
    刘春, 王宝军, 施斌, 等. 基于数字图像识别的岩土体裂隙形态参数分析方法[J]. 岩土工程学报, 2008, 30(9): 1383-1388. doi: 10.3321/j.issn:1000-4548.2008.09.021

    LIU Chun, WANG Bao-jun, SHI Bin, et al. Analytic method of morphological parameters of cracks for rock and soil based on image processing and recognition[J]. Chinese Journal of Geotechnical Engineering, 2008, 30(9): 1383-1388. (in Chinese) doi: 10.3321/j.issn:1000-4548.2008.09.021
    [14]
    王军祥, 曾相森, 徐晨晖, 等. 基于图像处理技术的岩体裂隙定量识别方法研究[J]. 地下空间与工程学报, 2022, 18(2): 446-457. https://www.cnki.com.cn/Article/CJFDTOTAL-BASE202202012.htm

    WANG Junxiang, ZENG Xiangsen, XU Chenhui, et al. Study on quantitative identification method of rock fracture based on image processing technology[J]. Chinese Journal of Underground Space and Engineering, 2022, 18(2): 446-457. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-BASE202202012.htm
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