Robust and Lightweight 3-D Reconstruction of Buried Threats Using Multiview GPR Data

Terrorist attacks pose a severe threat to global public security, among which preembedding offensive weapons in walls is an important attack type. Therefore, obtaining information about buried objects is crucial for addressing potential threats. Ground-penetrating radar (GPR), as an established nonintrusive detection method, is capable of effectively acquiring the 3-D information of buried objects. However, traditional GPR imaging methods often fail to complete the 3-D reconstruction task for targets under conditions of low signal-to-noise ratio (SNR). Moreover, the large computational load limits its application in real-time detection scenarios. To address these challenges, this article proposes a robust and lightweight 3-D reconstruction method for buried targets, specifically for pistols, eavesdropping devices, and explosives. The method first utilizes the 3-D Fourier transform of the wave equation to analyze the target wavefield information from 3-D C-scan data, which can obtain a preliminary visualization of the target, and then employs a projection mechanism to process the sparse energy-focused volume from multiple views. Finally, a multiview reconstruction algorithm is used to reconstruct the 3-D voxel model of the target. Compared to existing methods, our approach reduces parameters by 98.9% to 48.59M versus 3-D U-Net, floating-point operations (FLOPs) by 79.7% versus Kirchhoff migration, and achieves 0.575-s inference with a 0.829 Dice coefficient under optimal conditions. It maintains robust performance down to -5-dB SNR, with monotonic improvements in mean squared error (MSE), mean absolute error (MAE), and Dice as SNR increases. The experimental results indicate that the approach facilitates rapid and accurate retrieval of information on concealed dangerous objects in resource-limited settings.