Low-Cost Deep Learning-Based Prototype for Automatic Identification of Traffic Signs in Vehicles

  1. González, Enol García 1
  2. Villar, José R. 1
  3. de la Cal, Enrique 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Libro:
16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021)

ISSN: 2194-5357,  2194-5365

ISBN: 9783030878689,  9783030878696

Año de publicación: 2021

Páginas: 91-100

Tipo: Capítulo de Libro

DOI: 10.1007/978-3-030-87869-6_9 GOOGLE SCHOLAR

Resumen

The automotive industry has evolved in recent years with new driving assistance systems, which make it evolve towards autonomous driving, in which the figure of the driver becomes less relevant over time until it becomes unnecessary to have a person driving the vehicle. To continue with the development of autonomous cars, information surrounding the vehicle is necessary. As a contribution to this evolution towards autonomous driving, a low-cost prototype is presented, which can be installed in any type of car, capable of capturing images of driving employing a camera and processing information from traffic signs on the road. This information can be used as input for another system, in which, thanks to this already processed signal information, it can make other types of driving decisions. This prototype has been made using a set of 5 Deep Learning classifiers and implemented. As a result of the work, a prototype capable of detecting and classifying images with an accuracy of over 90% has been obtained. However, this prototype is not yet usable as it is not adapted to all environmental situations, for which future studies are proposed.

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Información de financiación

This research has been funded by the Spanish Ministry of Science and Innovation under project MINECO-TIN2017-84804-R, PID2020-112726RB-I00.

Financiadores

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