Abstract

Ground-penetrating radar (GPR), a highly efficient non-destructive detection method, finds extensive use in urban road underground target detection. Existing GPR data recognition algorithms often rely on singular time-domain spectrogram features, leading to potential misjudgements. To address this, we propose a novel algorithm based on sequence spectra and time-domain features. Serialized radar data, transformed through wavelets, is combined with time-domain images for input, enabling classification through a multi-scale convolutional neural network. Experiments show improved accuracy in underground target classification, offering a fresh perspective on intelligent GPR data recognition.

Details

Title
Ground penetrating radar urban road underground target classification algorithm using sequential spectral and time-domain features
Author
Li, F R 1 ; Shi, W X 1 ; Yang, F 1 ; Xu, M X 1 ; Fang, L 1 ; Fang, Y J 1 ; Wen, Y L 2 

 School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing, China 
 China Railway Guangzhou Group Co., Ltd ., Guangzhou, China 
First page
012011
Publication year
2024
Publication date
Nov 2024
Publisher
IOP Publishing
ISSN
17426588
e-ISSN
17426596
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3133731202
Copyright
Published under licence by IOP Publishing Ltd. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.