Rebar Clutter Suppression and Road Defects Localization in GPR B-Scan Images Based on SuppRebar-GAN and EC-Yolov7 Networks

Ground Penetrating Radar (GPR) is a non-destructive detection technology based on electromagnetic waves. However, the presence of clutter during the data acquisition process has a serious impact on the quality of GPR B-scan images. In particular, when strong clutter such as rebar mesh is present in the surface layer of the road area being scanned, it can be challenging to locate and identify road defects such as voids and faults in the acquired images. To suppress the surface rebar clutter and recover the disturbed defect echoes, we designed an unsupervised SuppRebar-GAN network. Meanwhile, to verify the accuracy of the location and type of its recovered echoes, we also set up an EC-Yolov7 network to localize and identify the recovered echoes and generate corresponding heatmaps based on the detection results to estimate the true location of the roadway defect in the subsurface. In this study, simulated and measured data of four typical road defects, namely void, fault, uncompact, and crack, were collected for training and testing the two networks. The experimental results show that both networks can perform their respective functions excellently without retraining in the face of test datasets collected in other simulations or real test scenarios that are very different from the initial training set.