期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 59, 期 8, 页码 6525-6536出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.3030079
关键词
3-D convolutional neural network (3-D-CNN); deep learning; ground penetrating radar (GPR); Kirchhoff migration; subsurface utility pipes
This article introduces a novel algorithm that combines 3-D convolutional neural network (3-D-CNN) and Kirchhoff migration for subsurface utility pipe detection using ground-penetrating radar (CPR). The developed model achieved a classification accuracy of about 91%, accurately estimating the pipes' existences and directions. The algorithm provides a clear understanding of pipes' 3-D positions and arrangement with reasonable calculation time based on experimental field data.
In this article, we focus on ground-penetrating radar (CPR) for subsurface utility pipe detection. Due to the dense and high-speed 3-D monitoring, GPR is a promising tool. However, because of enormous amount of radar data and difficulty of interpretation, inspection time and cost are the bottlenecks. In this article, we propose a novel detection algorithm by the combination of 3-D convolutional neural network (3-D-CNN) and Kirchhoff migration. A 3-D-CNN architecture was trained utilizing transverse and longitudinal pipes' measurement data. The classification accuracy of the developed model was about 91%, accurately estimating the pipes' existences and directions. The 3-D-CNN improved the classification accuracy by about 6% compared to 2-D-CNN in the case of transverse pipes by considering the 3-D geometries of the pipes. After box by-box search by 3-D-CNN, Kirchhoff migration was applied to cross section images and peaks were extracted. From the result of experimental field data, the algorithm provides the clear understandings of pipes' 3-D positions and arrangement with reasonable calculation time.
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