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赵鹏程
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Personal Information
  • Supervisor of Master's Candidates
  • Name (Pinyin):Zhao Pengcheng
  • Date of Birth:1993-09-05
  • E-Mail:
  • Date of Employment:2019-12-07
  • Administrative Position:高级实验师
  • Education Level:With Certificate of Graduation for Doctorate Study
  • Business Address:武汉大学信息学部遥感信息工程学院（5号楼）315办公室
  • Gender:Male
  • Contact Information:+86 15972003670
  • Status:Employed
  • Alma Mater:武汉大学
  • Teacher College:School of Remote Sensing and Information Engineering
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Paper Publications
Current position: Home   >   Scientific Research   >   Paper Publications

Deep learning based method for 3D reconstruction of underground pipes in 3D GPR C-scan data

  • Date of Publication:2025-01-07
  • Hits:

  • DOI number: 

    10.1016/j.tust.2024.105819

  • Affiliation of Author(s): 

    School of Remote Sensing and Information Engineering, Wuhan University, China

  • Journal: 

    TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY

  • Key Words: 

    Ground penetrating radar,C -scan data,Underground pipe reconstruction,Semantic segmentation model,Deep learning

  • Abstract: 

    Many urban underground locations suffer from incomplete or inaccurate records. Nevertheless, a comprehensive understanding of the subsurface conditions is crucial, particularly for pipe-related projects. Ground Penetrating Radar (GPR) serves as a widely-used nondestructive technique for detecting and locating underground objects. The efficient extraction of information from GPR data has been a research objective, especially in areas related to 3D GPR, which lacks sufficient investigation. In this paper, we propose a comprehensive workflow for constructing 3D models of underground pipes. Our approach differs from common classification tasks as it harnesses the 3D information within the GPR data and optimizes traditional 3D GPR processing methods. Additionally, for the semantic segmentation of pipes within 3D GPR data, we have made a series of attempts to improve it based on the 3D Unet network. Experimental results confirm the effectiveness of our method in reconstructing underground pipes using the 3D GPR data.

  • Co-author: 

    Huang,Ai,Yu, Qingwu,Hu, Ju,Zhang, Yibo,Zhou,Zhao, Yuchun, Mingyao, Fei, Pengcheng

  • Indexed by: 

    Journal paper

  • Discipline: 

    Engineering

  • Document Type: 

    J

  • Volume: 

    150

  • ISSN No.: 

    0886-7798

  • Translation or Not: 

    no

  • CN No.: 

    EI:20242116138451,WOS:001246517100001,Scopus:2-s2.0-85193809066

  • Date of Publication: 

    2024-08-01

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