PERTANIKA JOURNAL OF SOCIAL SCIENCES AND HUMANITIES

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• 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.