Shape-Aware Topological Representation for Pipeline Hyperbola Detection in GPR Data

Ground Penetrating Radar (GPR) is a widely used non-destructive testing (NDT) technique for subsurface exploration, particularly in infrastructure inspection. However, traditional interpretation methods often struggle with noise sensitivity and limited structural awareness. We propose a novel framework that integrates shape-aware topological features, derived from B-scan GPR images using Topological Data Analysis (TDA), with the object detection capabilities of a YOLOv5-based deep neural network (DNN). This topological representation improves geometric salience, enhancing detection and localization of underground utilities, especially pipelines. To mitigate the scarcity of annotated real-world data, a Sim2Real strategy is employed. Synthetic datasets are generated to capture both diverse subsurface conditions and the essential hyperbolic reflection patterns of pipelines, enabling more effective knowledge transfer to real-world scenarios. Experimental results show consistent improvements in mean Average Precision (mAP), highlighting the robustness and effectiveness of the proposed method. This work demonstrates the potential of TDA-enhanced deep learning for reliable subsurface object detection with broad implications in urban planning, safety inspection, and infrastructure management.