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Pattern Recognition and Image Analysis

, Volume 28, Issue 1, pp 155–162 | Cite as

Traffic Sign Classification with a Convolutional Network

  • A. Staravoitau
Applied Problems
  • 50 Downloads

Abstract

I approach the traffic signs classification problem with a convolutional neural network implemented in TensorFlow reaching 99.33% accuracy. The highlights of this solution would be data pre-processing, data augmentation pipeline, pre-training and skipping connections in the network. I am using Python as programming language and TensorFlow as a fairly low-level machine learning framework.

Keywords

machine learning classification convolutional network multi-scale features GTSDB traffic signs TensorFlow 

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References

  1. 1.
    P. Sermanet and Y. LeCun, “Traffic sign recognition with multi-scale convolutional networks,” in Proc. Int. Joint Conf. on Neural Networks (IJCNN’11) (San Jose, 2011).Google Scholar
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    M. Haloi, “Traffic sign classification using deep inception based convolutional networks” (2015). arXiv:1511.02992, 2015.Google Scholar
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    D. C. Ciresan, U. Meier, J. Masci, and J. Schmidhuber, “A committee of neural networks for traffic sign classification,” in Proc. Int. Joint Conf. on Neural Networks (IJCNN) (San Jose, 2011), pp. 1918–1921.Google Scholar
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    J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, “The German traffic sign recognition benchmark: a multi-class classification competition,” in Proc. Int. Joint Conf. on Neural Networks (San Jose, 2011).Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.Belarusian State UniversityMinskBelarus

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