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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.
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Details
1 School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing, China
2 China Railway Guangzhou Group Co., Ltd ., Guangzhou, China