The application of 3D ground-penetrating radar (3D-GPR) for subgrade distress detection has gained widespread popularity. To enhance the efficiency and accuracy of detection, pioneering studies have attempted to adopt automatic detection techniques, particularly deep learning. However, existing works typically rely on traditional 1D A-scan, 2D B-scan or 3D C-scan data of the GPR, resulting in either insufficient spatial information or high computational complexity. To address these challenges, we introduce a novel methodology for the subgrade distress detection task by leveraging the multi-view information from 3D-GPR data. Moreover, we construct a real multi-view image dataset derived from the original 3D-GPR data for the detection task, which provides richer spatial information compared to A-scan and B-scan data, while reducing computational complexity compared to C-scan data. Subsequently, we develop a novel \textbf{M}ulti-\textbf{V}iew \textbf{V}usion and \textbf{D}istillation framework, \textbf{GPR-MVFD}, specifically designed to optimally utilize the multi-view GPR dataset. This framework ingeniously incorporates multi-view distillation and attention-based fusion to facilitate significant feature extraction for subgrade distresses. In addition, a self-adaptive learning mechanism is adopted to stabilize the model training and prevent performance degeneration in each branch. Extensive experiments conducted on this new GPR benchmark demonstrate the effectiveness and efficiency of our proposed framework. Our framework outperforms not only the existing GPR baselines, but also the state-of-the-art methods in the fields of multi-view learning, multi-modal learning, and knowledge distillation. We will release the constructed multi-view GPR dataset with expert-annotated labels and the source codes of the proposed framework.

翻译：三维探地雷达（3D-GPR）在子结构病害检测中的应用已获得广泛认可。为提升检测效率与精度，前沿研究尝试采用自动检测技术，特别是深度学习。然而现有工作通常依赖传统的一维A扫描、二维B扫描或三维C扫描数据，导致存在空间信息不足或计算复杂度过高的问题。针对这些挑战，我们提出了一种利用三维探地雷达多视图信息的新型子结构病害检测方法。同时，基于原始三维探地雷达数据构建了真实的多视图图像检测数据集，该数据相较于A扫描与B扫描数据提供了更丰富的空间信息，同时相较于C扫描数据降低了计算复杂度。进一步地，我们开发了专门用于优化利用多视图探地雷达数据集的"多视图融合与蒸馏"框架（GPR-MVFD）。该框架创新性地融合了多视图蒸馏与基于注意力的融合机制，可有效提取子结构病害的关键特征。此外，采用自适应学习机制以稳定模型训练过程，防止各分支性能退化。在新型探地雷达基准数据集上的大量实验表明，所提框架不仅优于现有探地雷达基线方法，还超越了多视图学习、多模态学习及知识蒸馏领域的最新方法。我们将公开带专家标注的多视图探地雷达数据集及框架源代码。