Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards 3D Metric GPR Imaging Based on DNN Noise Removal and Dielectric
  Estimation   Ground Penetrating Radar (GPR) is one of the most important non-destructive
evaluation (NDE) devices to detect subsurface objects (i.e., rebars, utility
pipes) and reveal the underground scene. The two biggest challenges in
GPR-based inspection are the GPR data collection and subsurface target 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 deep neural network (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. We use both the field and synthetic
data to verify the proposed method. Experimental results demonstrate that our
proposed method can achieve better performance and faster processing speed in
GPR data collection and 3D GPR imaging than other methods.

33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th