Permittivity inversion of GPR images based on DeepLab

Hongwei Liu; Jinpeng Wang; Masud Talukdar; Da Yuan

doi:10.1117/12.2671696

28 April 2023 Permittivity inversion of GPR images based on DeepLab

Hongwei Liu, Jinpeng Wang, Masud Talukdar, Da Yuan

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126101H (2023) https://doi.org/10.1117/12.2671696
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

Ground Penetrating Radar (GPR) is usually used to detect unknown underground structure information, but it is very difficult to extract the underground target structure information from original GPR signal. This paper aims to solve the inversion problem by deep learning method. The two-dimensional ground penetrating radar B-scan signal is converted into intuitive underground structure information by neural network. In this paper, the DeepLab network proposed by Google is improved to solve the problem of permittivity inversion of GPR signal images. We verify the network using simulated data, which is generated by Finite Difference Time Domain (FDTD) algorithm. Finally, we quantitatively evaluate the performance of our network by comparing it with some existing deep learning inversion networks.

Citation Download Citation

Hongwei Liu, Jinpeng Wang, Masud Talukdar, and Da Yuan "Permittivity inversion of GPR images based on DeepLab", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126101H (28 April 2023); https://doi.org/10.1117/12.2671696

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