Construction Environment Noise Suppression of Ground-Penetrating Radar Signals Based on an RG-DMSA Neural Network

Ground-penetrating radar (GPR) is often used to detect targets in a construction environment. Due to the different construction environments, the noise exhibits different characteristics on the GPR signal. When the noise is widely distributed on the GPR signal, and its spectrum and the spectrum of the active signal are aliased, it is difficult to separate and suppress the noise by traditional filtering methods. In this paper, we propose a deep learning GPR image noise suppression method based on a recursive guided and dual multi-scale self-attention mechanism neural network (RG-DMSA-NN), which uses a recursive guidance module and a dual multi-scale self-attention mechanism module to improve the feature extraction ability of the image and enhance the robustness and generalization ability in image noise suppression. Through the application of noise suppression on the synthesized test data and the GPR data actually collected by the Macao Science and Technology Museum, the advantages of this method over the traditional filtering, DnCNN and UNet noise suppression methods are demonstrated.