用融合注意力机制提升雷达图像识别准确率，自动检测道路隐性缺陷。
Intelligent recognition of GPR road hidden defect images based on feature fusion and attention mechanism
- 结合多模态特征融合与全局注意力，增强缺陷特征表达。
- 在复杂背景下检测小目标、弱信号和高斯噪声，精度达92.8%以上。
- 适合智能交通与道路检测领域，尤其适用于数据稀缺场景。
地下雷达（GPR）已成为无损评估道路地下缺陷的关键工具。然而，传统GPR图像解读高度依赖主观经验，存在效率低、误差大的问题。本研究提出一套完整框架：(1) 基于DCGAN的数据增强策略，合成高保真度的GPR图像，在复杂背景中保持缺陷形态，缓解数据不足；(2) 提出多模态链与全局注意力网络（MCGA-Net），融合多尺度缺陷特征并引入全局注意力机制以增强上下文感知能力；(3) 采用MS COCO预训练模型进行迁移学习微调，加速收敛并提升泛化性能。消融实验与对比验证表明，MCGA-Net在精确率（92.8%）、召回率（92.5%）和mAP@50（95.9%）上表现优异。在高斯噪声、弱信号及小目标检测任务中仍具鲁棒性，优于现有模型。该工作建立了自动化GPR缺陷检测新范式，在复杂地下环境中实现高效高精度识别。
原文摘要 · Abstract (English)
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.
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