Novel architecture based on dual‐view fusion for underground hidden distresses detection

The detection of underground hidden distresses aims to locate the anomaly areas beneath the surface. This task faces significant challenges, including the scarcity of distress samples and the complex three-dimensional hidden structures of the distresses. Traditional approaches typically employ generative adversarial networks and manually designed object detectors to address these issues. Nonetheless, the current methods face challenges in maintaining semantic consistency between the generated samples and the real-world samples, and they can only detect anomalies based on features from a single image. To overcome these limitations, this paper proposes an innovative detection architecture that significantly enhances the performance of multi-object hidden distress detection by introducing a dual-view (horizontal and longitudinal) correlated generation model and a dual-stream detection mechanism. The approach offers a comprehensive subsurface analysis: The horizontal view captures large-scale anomalies, while the longitudinal view reveals vertical structural details, boosting multi-object distress detection. Specifically, a ground-penetrating radar image diffusion model (GPRDiff) is proposed to generate hidden distress images with dual-view correlation. Furthermore, this study designs a novel dual-view cross-information fusion transformer to achieve efficient fusion of dual-stream information. Experimental results demonstrate that using a combination of GPRDiff-generated images and real images as input, along with a joint-view guided dual-stream detector, significantly improves the detection accuracy of multi-object hidden distresses. This research not only fills the technical gap in multi-object generation within the field of 3D radar road detection but also provides new research insights and technical pathways for other detection industries.