Volume 37 Issue 3
Jun.  2023
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Article Navigation >  GEOTECHNICAL ENGINEERING TECHNIQUE  > 2023  >  37(3): 320-326
Shao Jianhong, Zheng Wanpeng, Zhang Bin, Du Yuan, Wang Xingtao, Luan Jihao. Disease Detection of Loess Highway Slope Based on Point Cloud Data[J]. GEOTECHNICAL ENGINEERING TECHNIQUE, 2023, 37(3): 320-326. doi: 10.3969/j.issn.1007-2993.2023.03.011
Citation: Shao Jianhong, Zheng Wanpeng, Zhang Bin, Du Yuan, Wang Xingtao, Luan Jihao. Disease Detection of Loess Highway Slope Based on Point Cloud Data[J]. GEOTECHNICAL ENGINEERING TECHNIQUE, 2023, 37(3): 320-326. doi: 10.3969/j.issn.1007-2993.2023.03.011
shu

Disease Detection of Loess Highway Slope Based on Point Cloud Data

doi: 10.3969/j.issn.1007-2993.2023.03.011
  • 1.

    GanSu Provincial Highway Development Group Co., Ltd., Lanzhou 730000, Gansu, China

  • 2.

    Gansu Erche Highway Project Management Co., Ltd., Qingyang 745000, Gansu, China

  • 3.

    Gansu Henglu Communication Survey and Design Institute Co., Ltd., Lanzhou 730000, Gansu, China

  • 4.

    Gansu Engineering Research Centre of Road Maintenance Technologies, Lanzhou 730000, Gansu, China

  • Received Date: 2022-06-19
  • Accepted Date: 2022-07-19
    • Available Online: 2023-08-08
  • Publish Date: 2023-06-08
    Abstract
  • Slope inspection is one of the main works of highway operation and maintenance. In view of the limitation of manual inspection and monitoring, a highway slope inspection and monitoring method based on 3D reconstruction and point cloud analysis using unmanned aerial vehicle (UAV)-based oblique photography technique was proposed. The results show that the terrain data of research area could be obtained quickly by the unmanned aerial vehicle (UAV)-based oblique photography technique. Slope diseases including slope deformation, gully erosion and blockage of drainage ditch could be identified quantitatively by 3D model reconstruction and point cloud data analysis. The algorithm of point cloud data analysis has a great impact on the results. Compared with Cloud to Cloud comparison (C2C) algorithm and Multiscale Model to Model Cloud comparison (M3C2) algorithm, the algorithm of Cloud to Mesh comparison (C2M) is most suitable for the point cloud data analysis of highway slope disease identification. This method improves the efficiency of slope inspection and is an effective method to make up for the shortage of manual inspection.

     

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