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Traffic Sign Classification with a Convolutional Network

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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.

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References

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Authors and Affiliations

  1. Belarusian State University, Minsk, Belarus

    A. Staravoitau

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  1. A. Staravoitau
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Correspondence to A. Staravoitau.

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Aliaksei (Alex) Staravoitau was born in Minsk, Belarus, in 1988. He received the B.E. degree in Mathematics from the Belarusian State University, Minsk, Belarus, in 2011, and is a first-year MSc student in computer science at the Belarusian State University of Informatics and Radioelectronics. Aliaksei has been an independent researcher since 2015, focusing mainly on applications of statistics and machine learning.

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Staravoitau, A. Traffic Sign Classification with a Convolutional Network. Pattern Recognit. Image Anal. 28, 155–162 (2018). https://doi.org/10.1134/S1054661818010182

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  • DOI: https://doi.org/10.1134/S1054661818010182

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