Abstract
Traffic Sign recognition is the technology that gives a vehicle the ability to recognize everyday traffic signs that are put on the road. Detection methods are usually classified as color based, shape based, and learning based methods. Recently, Convolutional Neural Networks (CNN) have shown to become the popular solution to image recognition problems. Thanks to the quick execution and high recognition performances the CNNs have greatly enhanced many computer vision tasks. In this paper, we propose a traffic sign recognition system by applying CNN architectures on the LISA traffic sign dataset.
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Acknowledgments
We would like to acknowledge the support from the Center of Excellence in Cybersecurity Research, Education and Outreach, North Carolina A&T State University.
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Brown, W.S., Roy, K., Yuan, X. (2021). US Traffic Sign Recognition Using CNNs. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1252. Springer, Cham. https://doi.org/10.1007/978-3-030-55190-2_50
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DOI: https://doi.org/10.1007/978-3-030-55190-2_50
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