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Description
GPR is one of the most important NDE devices to detect subsurface objects and reconstruct the underground scene. There are two challenges for GPR- based inspection, which are GPR data collection and subsurface object imaging. To address these challenges, we propose a robotic solution that automates the GPR data collection process with a free motion pattern. It facilitates the 3D metric GPR imaging by tagging the pose information with GPR measurement in real-time. We also introduce a DNN based GPR data analysis method which includes a noise removal segmentation module to clear the noise in GPR raw data and a DielectricNet to estimate the dielectric value of subsurface media in each GPR B-scan data.
Presentation Date
11 Aug 2021, 10:40-10:50 am
Meeting Name
INSPIRE-UTC 2021 Annual Meeting
Department(s)
Civil, Architectural and Environmental Engineering
Document Type
Poster
Document Version
Final Version
File Type
text
Language(s)
English
Improving 3D Metric GPR Imaging Using Robotic Data Collection and DNN based Processing
GPR is one of the most important NDE devices to detect subsurface objects and reconstruct the underground scene. There are two challenges for GPR- based inspection, which are GPR data collection and subsurface object imaging. To address these challenges, we propose a robotic solution that automates the GPR data collection process with a free motion pattern. It facilitates the 3D metric GPR imaging by tagging the pose information with GPR measurement in real-time. We also introduce a DNN based GPR data analysis method which includes a noise removal segmentation module to clear the noise in GPR raw data and a DielectricNet to estimate the dielectric value of subsurface media in each GPR B-scan data.
