A Lightweight 1D-CNN Architecture for Accurate and Efficient Road Type Classification Using Vibrational Signals

This paper addresses the challenge of road type classification using deep learning techniques applied to vibrational signals collected from inertial sensors. Two novel architectures, SepRNet-1D and SepSERNet-1D, are proposed to achieve high classification accuracy while maintaining computational eff...

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Detalhes bibliográficos

Principais autores:	Lorenzo Manoni, Simone Orcioni, Massimo Conti
Formato:	Artigo
Idioma:	Inglês
Publicado em:	IEEE 2025-01-01
coleção:	IEEE Access
Assuntos:	Deep learning convolutional neural networks separable convolution road condition monitoring vibrational signals inertial sensors
Acesso em linha:	https://ieeexplore.ieee.org/document/11193762/
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