Related papers: A Novel Shape-Aware Topological Representation for GPR Data with DNN Integration

A Novel Shape-Aware Topological Representation for GPR Data with DNN Integration

URL: http://arxiv.org/abs/2506.06311v1
Date: Mon, 26 May 2025 10:43:34 GMT
Title: A Novel Shape-Aware Topological Representation for GPR Data with DNN Integration
Authors: Meiyan Kang, Shizuo Kaji, Sang-Yun Lee, Taegon Kim, Hee-Hwan Ryu, Suyoung Choi,
Abstract summary: Ground Penetrating Radar (GPR) is a widely used Non-Destructive Testing (NDT) technique for subsurface exploration.<n>This study presents a novel framework that enhances the detection of underground utilities, especially pipelines.<n>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.
Score: 3.367318729981566
License: http://creativecommons.org/licenses/by/4.0/
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.

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