Size estimation of underground targets from GPR frequency spectra: A deep learning approach

Abstract

GPR (Ground Penetrating Radar) is a robust and effective device for identifying underground artefacts. Construction companies and civil engineers should be aware of the sizes of rebars and pipelines before and during construction work for various reasons. Most research efforts have typically concentrated on GPR signal analysis in the time domain, however recent studies have increasingly focused on analysis in the frequency domain. This paper proposes an artificial neural network (ANN) model for estimating the diameter of underground rods (solid) and pipes (hollow). GPR data captured in the time domain domain is transformed to the frequency domain using FFT, after which feature extraction is performed using ANN. An FPGA-based prototype GPR system is used to collect GPR A-Scan data for a variety of targets made of aluminium, stainless steel, rebar, and PVC. A mean absolute percentage error of 1.89% is achieved using the proposed model. The experimental results confirm the effectiveness of the proposed approach in extracting size-related information from GPR data.