论文标题
基于GPR的地下对象检测和重建，使用随机运动和Depthnet
GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet
论文作者
论文摘要
地面穿透雷达（GPR）是检测地下对象（即钢筋，实用管道）并揭示地下场景的最重要的非破坏性评估（NDE）设备之一。基于GPR检查的最大挑战之一是地下目标重建。为了解决此问题，本文提出了一个3D GPR迁移和介电预测系统，以检测和重建地下目标。 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使用Depthnet处理的GPR扫描数据以重建和可视化3D地下目标。我们提出的Depthnet通过删除B扫描图像中的噪声以及预测地下对象的深度来处理GPR数据。对于Depthnet模型训练和测试，我们在地球物理调查系统公司（GSSI）的混凝土测试坑中收集了实际GPR数据，并使用GPRMAX3.0模拟器创建合成GPR数据。我们创建的数据集包括350个标记的GPR图像。 B-SCAN特征检测的平均精度为92.64％，地下目标深度预测的平均误差为0.112。此外，与传统迁移方法相比，实验结果验证了我们提出的方法是否提高了生成3D GPR图像的迁移准确性和性能。
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.
