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

TL;DR

This paper introduces a shape-aware topological framework combining TDA and deep learning to improve pipeline detection in GPR data, addressing noise sensitivity and data scarcity.

Contribution

It presents a novel shape-aware topological representation integrated with YOLOv5 for enhanced underground utility detection, using synthetic data for training.

Findings

Significant improvement in detection accuracy (mAP)

Robustness to noise and structural variations

Effective Sim2Real domain adaptation

Abstract

Ground Penetrating Radar (GPR) is a widely used Non-Destructive Testing (NDT) technique for subsurface exploration, particularly in infrastructure inspection and maintenance. However, conventional interpretation methods are often limited by noise sensitivity and a lack of structural awareness. This study presents a novel framework that enhances the detection of underground utilities, especially pipelines, by integrating shape-aware topological features derived from B-scan GPR images using Topological Data Analysis (TDA), with the spatial detection capabilities of the YOLOv5 deep neural network (DNN). We propose a novel shape-aware topological representation that amplifies structural features in the input data, thereby improving the model's responsiveness to the geometrical features of buried objects. To address the scarcity of annotated real-world data, we employ a Sim2Real strategy that generates diverse and realistic synthetic datasets, effectively bridging the gap between simulated and real-world domains. Experimental results demonstrate significant improvements in mean Average Precision (mAP), validating the robustness and efficacy of our approach. This approach underscores the potential of TDA-enhanced learning in achieving reliable, real-time subsurface object detection, with broad applications in urban planning, safety inspection, and infrastructure management.