WITS 2020 pp 37-47 | Cite as

LiteNet: A Novel Approach for Traffic Sign Classification Using a Light Architecture

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 745)


This paper presents a deep convolutional neural network architecture to classify the traffic signs of the GTSRB dataset. Our method uses a very light architecture with a few number of parameters that achieve good results without the need of hard computation. To get at our goal, we use a filter bank. The aim of which being to extract more features, which will be used as input to a fully connected classifier. The recognition rate of our model gets an accuracy of 99.15%, overpassing the human performance being 98.81%. This way, LiteNet competes the best state of art architectures since our approach uses less memory and less computation.


Deep learning Traffic sign Network convolutional neural network Classification 


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© Springer Nature Singapore Pte Ltd. 2022

Authors and Affiliations

  1. 1.Mathematique and Applications Laboratory, Sciences and Techniques Faculty of MohammediaHassan 2 UniversityCasablancaMorocco

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