  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.
