Localization of Subsurface Pipes in Radar Images by 3D Convolutional Neural Network and Kirchhoff Migration

Ground Penetrating Radar (GPR) is a promising tool for subsurface utility pipe detection due to its dense and highspeed 3D monitoring. However, because of enormous amount of radar data and difficulty of interpretation, inspection time and cost are the bottlenecks. In this research, a novel detection algorithm by the combination of 3D Convolutional Neural Network (3D-CNN) and Kirchhoff migration was proposed. The developed model estimated pipes' existences and directions. 3D-CNN utilized the 3D geometries of the pipes to achieve high classification accuracy compared to 2D-CNN. Kirchhoff migration was applied to localize pipes by extracting peaks. From the result of experimental field data, the algorithm provides the clear understandings of pipes' 3D positions and arrangement with reasonable calculation time.