SIA OpenIR  > 工艺装备与智能机器人研究室
GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet
Feng, Jinglun1; Yang L(杨亮)1; Wang, Haiyan1; Song YF(宋屹峰)2
Department工艺装备与智能机器人研究室
Conference Name2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Conference DateMay 31 - August 31, 2020
Conference PlaceParis, France
Source Publication2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PublisherIEEE
Publication PlaceMay 2020
2020
Pages7035-7041
Indexed ByEI ; CPCI(ISTP)
EI Accession number20204309375525
WOS IDWOS:000712319504103
Contribution Rank2
ISBN978-1-7281-7395-5
AbstractGround Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) devices to detect the subsurface objects (i.e. rebars, utility pipes) and reveal the underground scene. One of the biggest challenges in GPR based inspection is the subsurface targets reconstruction. In order to address this issue, this paper presents a 3D GPR migration and dielectric prediction system to detect and reconstruct underground targets. This system is composed of three modules: 1) visual inertial fusion (VIF) module to generate the pose information of GPR device, 2) deep neural network module (i.e., DepthNet) which detects B-scan of GPR image, extracts hyperbola features to remove the noise in B-scan data and predicts dielectric to determine the depth of the objects, 3) 3D GPR migration module which synchronizes the pose information with GPR scan data processed by DepthNet to reconstruct and visualize the 3D underground targets. Our proposed DepthNet processes the GPR data by removing the noise in B-scan image as well as predicting depth of subsurface objects. For DepthNet model training and testing, we collect the real GPR data in the concrete test pit at Geophysical Survey System Inc. (GSSI) and create the synthetic GPR data by using gprMax3.0 simulator. The dataset we create includes 350 labeled GPR images. The DepthNet achieves an average accuracy of 92.64% for B-scan feature detection and an 0.112 average error for underground target depth prediction. In addition, the experimental results verify that our proposed method improve the migration accuracy and performance in generating 3D GPR image compared with the traditional migration methods.
Language英语
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/27761
Collection工艺装备与智能机器人研究室
Affiliation1.City College of New York, Electrical Engineering Department, New York, United States
2.University of Chinese Academy of Sciences, Shenyang Institute of Automation, Chinese Academy of Sciences ,China
Recommended Citation
GB/T 7714
Feng, Jinglun,Yang L,Wang, Haiyan,et al. GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet[C]. May 2020:IEEE,2020:7035-7041.
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