Abstract
The transportation system has become a fascinating and active research topic due to its multiple problems; most prior research has focused on traffic forecasting, the advanced driver assistant system (ADAS), and self-driving vehicles. Traffic sign recognition (TSR) is an essential sub-system in ADAS that helps a driver better understand the surrounding environment (obstacles, frost, pedestrians). Automatic recognition of traffic signs is a real-world computer vision challenge and pattern recognition problem. Recently, deep architecture neural networks have shown robust solutions in many areas (health care, agriculture, transportation) due to their ability to handle large amounts of data and excel in complex systems. Therefore, a convolutional neural network (CNN) has been adopted, one of the best deep learning approaches in pattern recognition and image classification for TSR. The proposed architecture has been trained and tested on the German traffic sign recognition benchmark dataset (GTSRB). The results reported accuracy of 99.43% that outperformed human accuracy.
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Acknowledgements
This work is part of the project “SAFEROAD Meta-plateforme pour la Sécurité Routiére (MSR)” which is supported by the METLE and the National Center of the Scientific and Technical Research (CNRST) under contract No: 24/2017.
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Khyara, H., Amine, A., Nassih, B. (2022). A Novel Deep ConvNets Architecture for Traffic Sign Recognition. In: Bansal, J.C., Engelbrecht, A., Shukla, P.K. (eds) Computer Vision and Robotics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-8225-4_36
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DOI: https://doi.org/10.1007/978-981-16-8225-4_36
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