Traffic Sign Recognition with WiSARD and VG-RAM Weightless Neural Networks

Authors

  • Mariella Berger Departamento de Informática
  • Avelino Forechi Departamento de Informática
  • Alberto F. De Souza Departamento de Informática
  • Jorcy de Oliveira Neto Departamento de Informática
  • Lucas Veronese Departamento de Informática
  • Victor Neves Departamento de Informática
  • Edilson de Aguiar Departamento de Informática
  • Claudine Badue Departamento de Informática

Keywords:

Traffic Sign Recognition, VG-RAM Weightless Neural Networks, WiSARD, Log-Polar Mapping from Retina to Primary Visual Cortex (V1), German Traffic Sign Recognition Benchmark

Abstract

We present two biologically inspired approaches to traffic sign recognition based on Weightless Neural Networks (WNN): one based on Virtual Generalizing Random Access Memory (VG-RAM) neurons and the other on the Wilkes, Stonham and Aleksander Recognition Device (WiSARD) neurons. Both approaches employ the same neural architecture that models the transformations suffered by the images captured by the eyes from the retina to the primary visual cortex (V1) of the mammalian brain. We evaluated the performance of both approaches on the German Traffic Sign Recognition Benchmark (GTSRB). Our system based on VG-RAM neurons achieved a performance significantly better than the one based on WiSARD neurons and was ranked fifth in the GTSRB (the third and fourth places were human classifiers) with a recognition rate of 98.42%.

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Published

2013-01-01

How to Cite

Mariella Berger, Avelino Forechi, Alberto F. De Souza, Jorcy de Oliveira Neto, Lucas Veronese, Victor Neves, … Claudine Badue. (2013). Traffic Sign Recognition with WiSARD and VG-RAM Weightless Neural Networks. Journal of Network and Innovative Computing, 1, 12. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/19

Issue

Vol. 1 (2013)

Section

Original Article

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