GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 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, Jinglun 2; Yang L(杨亮)2; Wang, Haiyan 2; Song YF(宋屹峰)1
	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
内容类型	会议论文
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.University of Chinese Academy of Sciences, Shenyang Institute of Automation, Chinese Academy of Sciences ,China 2.City College of New York, Electrical Engineering Department, New York, United States
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.