GPRTransNet: A deep learning–based ground-penetrating radar translation network

Abstract

Ground-penetrating radar (GPR) is an essential nondestructive testing tool widely used in tunnel and road defect detection, underground object detection, and unstructured terrain perception. Currently, most GPR inversion methods based on deep learning use convolutional neural networks, which have limitations such as incomplete feature extraction and low accuracy. Inspired by advancements in the NLP field, this paper proposes a novel deep learning framework for GPR called GPRTransNet. The algorithm introduces a “wave to permittivity” translation architecture, leveraging the “memory” function of recurrent neural networks to translate GPR data into permittivity model images, similar to language translation. In addition, the inclusion of the attention mechanism significantly enhances the network’s ability to represent defects in complex scenarios, resulting in outstanding translation performance. GPRTransNet has been validated on a simulated dataset, with the newly proposed G-SSIM evaluation metric showing that the permittivity similarity of GPRTransNet-Lite reaches 98.47% and that of GPRTransNet-Pro is 99.26%. Furthermore, GPRTransNet demonstrates good translation performance on real data. Experimental results show that the results of GPRTransNet’s translation of GPR are satisfactorily matched.