Detection of vertical traffic signaling with convolutional neural networks based on residual blocks

• Adrian Javier Alarcon Vargas

  , Instituto de Investigaciones en Ciencia y Tecnología, Universidad La Salle, Bolivia

  Author

  https://orcid.org/0000-0002-4716-4112 (unauthenticated)

The objective of the present work is to train a neural network capable of detecting vertical traffic signaling and classify it using residual blocks, as they allow deeper neural networks. The methodology used for the development of the neural network comprises four phases: neural network definition, training, utilization, and maintenance of the neural network. For the development of the
neural network there are two datasets, the first is of German origin, consists of 50,000 images and is widely used for the  classification of traffic signs; and the second of Bolivian origin, which has 9,548 road images. The percentage of efficiency of the
neural network no. 1 with the GTSRB dataset is high, obtaining a value of 94.36%, it also includes high values in the classification report, otherwise, it happens with the Bolivia dataset because the dataset is unbalanced.

1. Adrian Javier Alarcon Vargas, , Instituto de Investigaciones en Ciencia y Tecnología, Universidad La Salle, Bolivia

   Ingeniero de Sistemas con especialización en desarrollo de aplicaciones web y aplicaciones
   móviles. Miembro del Instituto de Investigación en Ciencia y Tecnología de la Universidad La Salle.
   ORCID: https://orcid.org/0000-0002-4716-4112

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• 2022-09-30 (2)
• 2024-04-04 (1)

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