A Novel Shape-Aware Topological Representation for GPR Data with DNN Integration
Journal:
arXiv
Published Date:
May 26, 2025
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.
