探地雷达多阶段级联U-Net墙内小目标三维重建方法

Ground Penetrating radars (GPR) are essential for detecting buried targets in civilian and military applications, especially given the increasing demand for detecting and imaging small targets within walls. The complex structures and materials of walls pose substantial challenges for precisely reconstructing small targets. To address this issue, this study proposes a multistage cascaded U-Net approach for the three-dimensional reconstruction of small targets within walls. First, we developed a high-resolution detection model and a dataset tailored to handle complex wall scenes. Thereafter, using the Monte Carlo sampling method, we sampled aggregate particle sizes to create a physical three-dimensional aggregate scattering model that satisfies grading requirements, thus enhancing the realism and accuracy of the simulated scenes. Our multistage network design effectively suppresses noise and inhomogeneous clutter in C-scan data, thereby improving signal quality. The preprocessed data are then fed into subsequent network stages to reconstruct the distribution of three-dimensional reconstruction values. In addition, we proposed an adaptive multiscale module and a cascaded network training strategy to better fit small target information in complex scenes. Through comparisons with simulated and measured data, we confirmed the effectiveness and generalizability of our method. Unlike existing techniques, our approach successfully reconstructs small targets within three-dimensional walls, thereby considerably enhancing the peak signal-to-noise ratio and providing critical technical support for accurately detecting small targets within walls.