Abstract
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.
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Naim, S., Moumkine, N. (2022). LiteNet: A Novel Approach for Traffic Sign Classification Using a Light Architecture. In: Bennani, S., Lakhrissi, Y., Khaissidi, G., Mansouri, A., Khamlichi, Y. (eds) WITS 2020. Lecture Notes in Electrical Engineering, vol 745. Springer, Singapore. https://doi.org/10.1007/978-981-33-6893-4_4
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DOI: https://doi.org/10.1007/978-981-33-6893-4_4
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