A Method for Traffic Sign Recognition with CNN using GPU

Alexander Shustanov, Pavel Yakimov

2017

Abstract

In recent years, the deep learning methods for solving classification problem have become extremely popular. Due to its high recognition rate and fast execution, the convolutional neural networks have enhanced most of computer vision tasks, both existing and new ones. In this article, we propose an implementation of traffic signs recognition algorithm using a convolution neural network. Training of the neural network is implemented using the TensorFlow library and massively parallel architecture for multithreaded programming CUDA. The entire procedure for traffic sign detection and recognition is executed in real time on a mobile GPU. The experimental results confirmed high efficiency of the developed computer vision system.

Paper Citation

in Harvard Style

Shustanov A. and Yakimov P. (2017). A Method for Traffic Sign Recognition with CNN using GPU . In Proceedings of the 14th International Joint Conference on e-Business and Telecommunications - Volume 1: SIGMAP, (ICETE 2017) ISBN 978-989-758-260-8, pages 42-47. DOI: 10.5220/0006436100420047

in Bibtex Style

@conference{sigmap17,
author={Alexander Shustanov and Pavel Yakimov},
title={A Method for Traffic Sign Recognition with CNN using GPU},
booktitle={Proceedings of the 14th International Joint Conference on e-Business and Telecommunications - Volume 1: SIGMAP, (ICETE 2017)},
year={2017},
pages={42-47},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006436100420047},
isbn={978-989-758-260-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications - Volume 1: SIGMAP, (ICETE 2017)
TI - A Method for Traffic Sign Recognition with CNN using GPU
SN - 978-989-758-260-8
AU - Shustanov A.
AU - Yakimov P.
PY - 2017
SP - 42
EP - 47
DO - 10.5220/0006436100420047