Abstract
Ground Penetrating Radar (GPR) has emerged as a pivotal tool for non-destructive evaluation of subsurface road defects. However, conventional GPR image interpretation remains heavily reliant on subjective expertise, introducing inefficiencies and inaccuracies. This study introduces a comprehensive framework to address these limitations: (1) A DCGAN-based data augmentation strategy synthesizes high-fidelity GPR images to mitigate data scarcity while preserving defect morphology under complex backgrounds; (2) A novel Multi-modal Chain and Global Attention Network (MCGA-Net) is proposed, integrating Multi-modal Chain Feature Fusion (MCFF) for hierarchical multi-scale defect representation and Global Attention Mechanism (GAM) for context-aware feature enhancement; (3) MS COCO transfer learning fine-tunes the backbone network, accelerating convergence and improving generalization. Ablation and comparison experiments validate the framework's efficacy. MCGA-Net achieves Precision (92.8%), Recall (92.5%), and mAP@50 (95.9%). In the detection of Gaussian noise, weak signals and small targets, MCGA-Net maintains robustness and outperforms other models. This work establishes a new paradigm for automated GPR-based defect detection, balancing computational efficiency with high accuracy in complex subsurface environments.
Abstract (translated)
地面穿透雷达（GPR）已成为非破坏性评估道路地下缺陷的重要工具。然而，传统的GPR图像解读仍然严重依赖于主观的专业知识，导致效率低下和不准确的问题。本研究提出了一套全面的框架来解决这些问题：(1) 一种基于DCGAN的数据增强策略可以合成高保真的GPR图像，以缓解数据稀缺问题，并在复杂背景下保持缺陷形态；(2) 提出了一个新颖的多模态链全球注意力网络（MCGA-Net），它整合了多模态链特征融合（MCFF）用于层次化的多尺度缺陷表示以及全局注意力机制（GAM）来增强上下文感知特性；(3) 利用MS COCO迁移学习微调骨干网络，以加速收敛并提高泛化能力。消融和对比实验验证了该框架的有效性。MCGA-Net在精度、召回率及mAP@50指标上分别达到了92.8%、92.5% 和 95.9%。在检测高斯噪声、弱信号以及小目标时，MCGA-Net保持了鲁棒性和性能优势。这项工作为基于GPR的自动缺陷检测建立了一个新的范式，在复杂的地下环境中实现了计算效率和高精度之间的平衡。
URL
https://arxiv.org/abs/2512.21452
