GPR Bscan Imaging Enhancement Method for Rebar Occlusion

Abstract

When using ground-penetrating radar (GPR) to detect targets below shallow rebar mesh in reinforced concrete structures, the strong scattering characteristics of rebar mesh cause distortion and interference of target echoes and lead to imaging artifacts and degradation. This letter proposes a coarse-scale and fine-scale dual-branch imaging enhancement network (CFD-IENet) to achieve target imaging under rebar mesh in reinforced concrete by combining Bscan echo data enhancement with back projection (BP) imaging result enhancement. First, a residual U (Res-U) network suppresses complex background clutter in Bscan data to improve the signal-to-noise ratio. Then, a coarse-scale and fine-scale dual-branch network is constructed to enhance both Bscan and BP imaging. In the Bscan enhancement stage, strong and weak signals are trained separately, aiming for surface rebar echo interference in reconstructing weak target signals beneath the rebar mesh. In the BP imaging enhancement stage, artifacts and multipath ghosts are suppressed to enhance occluded target imaging. A bilinear fusion module (BFM) is designed to facilitate global feature interaction, promoting the fusion of Bscan and BP imaging features across scales, thereby improving reconstruction and enhancement accuracy. The experimental results on cracks occluded by rebar mesh demonstrate the method’s effectiveness, showing a 4.73-dB improvement in peak signal-to-noise ratio (PSNR) and a 0.16 improvement in structural similarity (SSIM) index compared to the RNMF + BP + Unet enhancement method.