Low-Cost Surface Classification System Supported by Deep Neural Models

Autor:	Sánchez Andrades, Ignacio, Velasco Garcías, Juan María, Sánchez Lozano, Miguel, Cabrera Carrillo, Juan Antonio, Castillo Aguilar, Juan Jesús
Rok vydání:	2021
Předmět:	Surface estimation Datos - Adquisición Aprendizaje automático (Inteligencia artificial) Vibrations Machine learning Automóviles Data acquisition Automobile systems
Zdroj:	RIUMA. Repositorio Institucional de la Universidad de Málaga instname
Popis:	Determining the surface on which a vehicle is moving is vital information for im-proving active safety systems. Performing the surface classification or estimating adherence through tire slippage can lead to late action in possible risk situations. Currently, approaches based on image, sound, or vibration analysis are emerging as a viable alternative, though sometimes complex. This work proposes a methodology based on the use of low-cost accelerometers combined with Deep Learning tech-niques. The performance of the proposed system is evaluated with real tests, where high percentages of accuracy are obtained in the classification task. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=RECOLECTA___::013cd4b592cc70a5e13944a36727e95c https://hdl.handle.net/10630/22969 Zobrazit plný text záznamu