GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet
文献类型：会议论文
| 作者 | Feng, Jinglun1; Yang L(杨亮)1 ; Wang, Haiyan1; Song YF(宋屹峰)2
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| 出版日期 | 2020 |
| 会议日期 | May 31 - August 31, 2020 |
| 会议地点 | Paris, France |
| 页码 | 7035-7041 |
| 英文摘要 | Ground 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. |
| 产权排序 | 2 |
| 会议录 | 2020 IEEE International Conference on Robotics and Automation, ICRA 2020
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | May 2020 |
| 语种 | 英语 |
| ISBN号 | 978-1-7281-7395-5 |
| WOS记录号 | WOS:000712319504103 |
| 源URL | [http://ir.sia.cn/handle/173321/27761]  |
| 专题 | 工艺装备与智能机器人研究室 |
| 作者单位 | 1.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 |
| 推荐引用方式 GB/T 7714 | Feng, Jinglun,Yang L,Wang, Haiyan,et al. GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet[C]. 见:. Paris, France. May 31 - August 31, 2020. |
入库方式： OAI收割
来源：沈阳自动化研究所
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