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Real-Time Traffic Sign Recognition Using Deep Learning

Ananya Belagodu Shivayogi, Nehal Chakravarthy Matasagara Dharmendra, Anala Maddur Ramakrishna and Kolala Nagaraju Subramanya

Pertanika Journal of Tropical Agricultural Science, Volume 31, Issue 1, January 2023

DOI: https://doi.org/10.47836/pjst.31.1.09

Keywords: DeepStream, Indian traffic sign dataset, NVIDIA Jetson Nano, traffic sign detection, YOLOv4

Published on: 3 January 2023

Traffic Sign Recognition (TSR) is one of the most sought-after topics in computer vision, mostly due to the increasing scope and advancements in self-driving cars. In our study, we attempt to implement a TSR system that helps a driver stay alert during driving by providing information about the various traffic signs encountered. We will be looking at a working model that classifies the traffic signs and gives output in the form of an audio message. Our study will be focused on traffic sign detection and recognition on Indian roads. A dataset of Indian road traffic signs was created, based upon which our deep learning model will work. The developed model was deployed on NVIDIA Jetson Nano using YOLOv4 architecture, giving an accuracy in the range of 54.68–76.55% on YOLOv4 architecture. The YOLOv4-Tiny model with DeepStream implementation achieved an FPS of 32.5, which is on par with real-time detection requirements.

  • Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv Preprint. http://arxiv.org/abs/2004.10934

  • Do, H. N, Vo, M. T., Luong, H. Q., Nguyen, A. H., Trang, K., & Vu, L. T. K. (2017). Speed limit traffic sign detection and recognition based on support vector machines. In 2017 International Conference on Advanced Technologies for Communications (ATC) (pp. 274-278). IEEE Publishing. https://doi.org/10.1109/ATC.2017.8167633

  • Ellahyani, A., El Ansari, M., El Jaafari, I., & Charfi, S. (2016). Traffic sign detection and recognition using features combination and random forests. International Journal of Advanced Computer Science and Applications, 7(1), 686-693. https://doi.org/10.14569/IJACSA.2016.070193

  • Hasegawa, R., Iwamoto, Y., & Chen, Y. W. (2019). Robust detection and recognition of japanese traffic sign in the complex scenes based on deep learning. In 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE) (pp. 575-578). IEEE Publishing. https://doi.org/10.1109/GCCE46687.2019.9015419

  • Hegadi, R. S. (2011). Automatic traffic sign recognition. In Proceedings of International Conference on Communication, Computation, Management & Nanotechnology (pp. 1-5). REC Bhalki.

  • Islam, M. T. (2019). Traffic sign detection and recognition based on convolutional neural networks. In 2019 International Conference on Advances in Computing, Communication and Control (ICAC3) (pp. 1-6). IEEE Publishing. https://doi.org/10.1109/ICAC347590.2019.9036784

  • Koresh, M. H. J. D. (2019). Computer vision based traffic sign sensing for smart transport. Journal of Innovative Image Processing, 1(1), 11-19. https://doi:10.36548/jiip.2019.1.002

  • Lu, X., Kang, X., Nishide, S., & Ren, F. (2019). Object detection based on SSD-ResNet. In 2019 IEEE 6th International Conference on Cloud Computing and Intelligence Systems (CCIS) (pp. 89-92). IEEE Publishing. https://doi.org/10.1109/CCIS48116.2019.9073753

  • Luo, H., Yang, Y., Tong, B., Wu, F., & Fan, B. (2018). Traffic sign recognition using a multi-task convolutional neural network. IEEE Transactions on Intelligent Transportation Systems, 19(4), 1100-1111. https://doi.org/10.1109/TITS.2017.2714691

  • Muthukumaresan, T., Kirubakaran, B., Kumaresan, D., Satheesan, A., & Prakash, A. J. (2016). Recognition of traffic sign using support vector machine and fuzzy cluster. International Journal of Science Technology & Engineering, 2(10), 190-197.

  • Oltean, G., Florea, C., Orghidan, R., & Oltean, V. (2019). Towards real time vehicle counting using YOLO-Tiny and fast motion estimation. In 2019 IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME) (pp. 240-243). IEEE Publishing. https://doi.org/10.1109/SIITME47687.2019.8990708

  • Sari, A., & Cibooglu, M. (2018). Traffic sign detection and recognition system for autonomous RC cars. In 2018 6th International Conference on Control Engineering & Information Technology (CEIT) (pp. 1-5). IEEE Publishing. https://doi.org/10.1109/CEIT.2018.8751898

  • Tabernik, D., & Skocaj, D. (2020). Deep learning for large-scale traffic-sign detection and recognition. IEEE Transactions on Intelligent Transportation Systems, 21(4), 1427-1440. https://doi.org/10.1109/TITS.2019.2913588

  • Zhang, J., Huang, M., Jin, X., & Li, X. (2017). A real-time Chinese traffic sign detection algorithm based on modified YOLOv2. Algorithms, 10(4), Article 127. https://doi.org/10.3390/a10040127

  • Zhong, Z., Zheng, L., Kang, G., Li, S., & Yang, Y. (2020). Random erasing data augmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 13001-13008. https://doi.org/10.1609/aaai.v34i07.7000

  • Zoph, B., Cubuk, E. D., Ghiasi, G., Lin, T. Y., Shlens, J., & Le, Q. V. (2019). Learning data augmentation strategies for object detection. arXiv Preprint.

ISSN 1511-3701

e-ISSN 2231-8542

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