Improving 3D Metric GPR Imaging Using Automated Data Collection and Learning-Based Processing
Improving 3D Metric GPR Imaging Using Automated Data Collection and Learning-Based Processing
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DOI：
10.1109/jsen.2022.3164707
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发表时间：
2023-03
影响因子：
4.3
通讯作者：
Jinglun Feng;Liang Yang;Ejup Hoxha;Jizhong Xiao
中科院分区：
文献类型：
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作者：
Jinglun Feng;Liang Yang;Ejup Hoxha;Jizhong Xiao
探地雷达(GPR)是探测地下物体(如钢筋、管线)和重建地下场景的重要无损检测设备之一。探地雷达探测面临着两大挑战，即探地雷达数据采集和地下目标三维成像。为了应对这些挑战，我们首先提出了一种机器人解决方案，该解决方案使用自由运动模式自动执行探地雷达数据收集过程。它通过实时地用探地雷达测量来标记姿态，从而简化了3D度量探地雷达成像。此外，为了提高三维探地雷达成像质量，我们提出了一种基于学习的探地雷达数据分析方法，该方法包括去除原始探地雷达数据中的背景噪声的去噪模块和估计每个探地雷达B超数据中地下介质介电值的卷积递归神经网络(CRNN)。我们使用现场数据和合成数据来验证所提出的方法。实验结果表明，在三维探地雷达成像中，我们提出的方法比基线方法具有更高的性能和更快的处理速度。
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 reconstruct the underground scene. There are two challenges for GPR-based inspection, which are GPR data collection and 3D subsurface object imaging. To address these challenges, we first 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 with GPR measurement in real-time. Moreover, to improve the 3D GPR imaging, we introduce a learning-based GPR data analysis method, which includes a noise removal module to clear the background noise in raw GPR data and a Convolutional Recurrent Neural Network (CRNN) to estimate the dielectric value of subsurface medium in each GPR B-scan data. We use both field and synthetic data to verify the proposed methods. Experimental results demonstrate that our proposed methods can achieve higher performance and faster processing speed in 3D GPR imaging than baseline methods.