Subsurface Pipes Detection Using DNN-Based Back Projection on GPR Data

Jinglun Feng, Liang Yang, Haiyan Wang, Yingli Tian, Jizhong Xiao; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 266-275

Localization and reconstruction of underground targets, the problem of estimating the position and geometry of the objects from Ground Penetration Radar (GPR), still lies at the core of non-destructive testing (NDT). In this paper, we present MigrationNet, a learning-based approach to detect and visualize subsurface objects. Compared with the existing learning-based method of GPR, our proposed approach could not only detect the hyperbola feature in the raw B-scan image but also interpret hyperbola features into the cross-section image of subsurface pipes. Furthermore, to compare the proposed method with the conventional back-projection methods for GPR data interpretation, a synthetic GPR dataset that mimics the real NDT environment is also introduced in this work. The study indicates the effectiveness of our method, it uses less GPR data for underground pipes reconstruction, produces better GPR imaging results with less computation, and shows the robustness to noise.