  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.
