-
Adam A. & Ioannidis C. (2014). Automatic road-sign detection and classification based on support vector machines and HOG descriptors. ISPRS Annals of the photogrammetry, remote sensing and spatial information sciences. 2 (5): 1–7.
-
Artamonov N.S., & Yakimov P.Y. (2018). Towards real-time traffic sign recognition via YOLO on a mobile GPU. Journal of Physics: Conference Series. 1096(1): 012086-012095.
-
Bochkovskiy A., Wang C. Y., & Liao H. Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934. 1-17.
-
Chung Yu Wang & Royce Cheng-Yue (2016). Traffic sign detection using You Only Look Once framework. Stanford University. 1-9.
-
Dewi C., Chen R. C., Liu Y. T., Jiang X., & Hartomo K. D. (2021). Yolo v4 for advanced traffic sign recognition with synthetic training data generated by various gan. IEEE Access. (9): 97228-97242.
-
Do Thuan (2021). Thesis: Evolution of YOLO Algorithm and YOLOv5: The State-of-the-art Object Detection Algorithm. Oulu University of Applied Sciences.
-
Gaoli Hu & Chengyu Wen (2021). Traffic sign detection and recognition based on improved YOLOv4 algorithm. International journal of computer applications technology and research. 10 (06): 161-165.
-
Girshick R., Donahue J., Darrell T., & Malik J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on computer vision and pattern recognition. 580–587.
-
Girshick R. (2015). Fast R-CNN. IEEE International Conference on Computer. Vision (ICCV). 1440-1448.
-
Phạm Phú Khương & Hà Đắc Bình (2018). Nhận dạng biển báo giao thông Việt Nam với thuật toán PCA kết hợp LDA. Tạp chí Khoa học và công nghệ, Trường Đại học Duy Tân. 01(26): 84-93.
-
Redmon J., Divvala S., Girshick R., & Farhadi A. (2016). You only look once: Unified, real-time object detection. 2016 IEEE Conference on computer vision and pattern recognition. 779-788.
-
Ren S., He K., Girshick R., & Sun J. (2017). Faster R-CNN: Towards real-time object detection with region proposal net works. IEEE Transactions on Pattern Analysis and Machine Intelligence. 39(6): 1137-1149.
-
Satılmış Y., Tufan F., Şara M., Karslı M., Eken S., & Sayar A. (2018, September). CNN based traffic sign recognition for mini autonomous vehicles. International conference on information systems architecture and technology, Springer, Cham. 85-94.
-
Shao F., Wang X., Meng F., Rui T., Wang D., & Tang J. (2018). Real-time traffic sign detection and recognition method based on simplified Gabor wavelets and CNNs. Sensors. 18(10): 3192-3215.
-
Sichkar V.N. & Kolyubin S.A (2020). Real time detection and classification of traffic signs based on YOLO version 3 algorithm. Scientific and technical journal of information technologies, Mechanics and Optics. 20(3): 418–424.
-
Shustanov A. & Yakimov P. (2017). CNN design for real-time traffic sign recognition. Procedia engineering. 201: 718-725.
-
Trương Quốc Bảo, Trương Hùng Chen & Trương Quốc Định (2015). Phát hiện và nhận dạng biển báo giao thông đường bộ sử dụng đặc trưng HOG và mạng nơron nhân tạo. Tạp chí Khoa học Trường Đại học Cần Thơ, Số chuyên đề: Công nghệ Thông tin (2015). 47-54.
-
Vokhidov H., Hong H. G., Kang J. K., Hoang T. M., & Park K. R. (2016). Recognition of damaged arrow-road markings by visible light camera sensor based on convolutional neural network. Sensors. 16(12): 2160-2175.
-
Wang L., Zhou K., Chu A., Wang G., & Wang L. (2021). An improved light-weight traffic sign recognition algorithm based on YOLOv4-Tiny. IEEE Access. 9: 124963-124971.
