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.

翻译：探地雷达是一种广泛应用于地下探测的非破坏性检测技术，尤其在基础设施检测与维护领域。然而，传统解释方法常受限于噪声敏感性及结构感知能力的不足。本研究提出一种新颖框架，通过将基于B扫描探地雷达图像、利用拓扑数据分析提取的形状感知拓扑特征，与YOLOv5深度神经网络的空间检测能力相结合，显著提升地下管线（特别是管道）的检测性能。我们提出了一种创新的形状感知拓扑表示方法，该表示能增强输入数据中的结构特征，从而提高模型对埋藏物体几何特征的响应能力。针对标注真实数据稀缺的问题，我们采用Sim2Real策略生成多样且逼真的合成数据集，有效弥合了仿真域与真实域之间的差距。实验结果表明，该方法在平均精度均值指标上取得显著提升，验证了我们方法的鲁棒性与有效性。该研究凸显了拓扑数据分析增强学习在实现可靠、实时地下物体检测方面的潜力，在城市规划、安全检测及基础设施管理等领域具有广泛应用前景。