• Nguyen Thi Nhung Hung Yen University of Technology and Education
  • Bui Thi Kim Thoa Hung Yen University of Technology and Education
  • Tran Long Quang Anh Hanoi University of Science and Technology
  • Nguyen Phu Dat Hanoi University of Industry
Keywords: Convolutional neural networks (CNNs), deep learning, YOLO, YOLOv3, image recognition, traffic sign recognition


Research paper on convolutional neural networks, YOLOv3 algorithm, a deep learning model which is being widely researched and developed in the field of computer vision for real-time objects recognition. Applying this algorithm to build an identification system, analysis, and build a neural network to recognize traffic signs. The results of the paper are based on the model that has been built, tested, and evaluated. The results show that using the model in the image recognition problem is completely appropriate. In addition, they are the basis for the research and development of convolutional neural networks in recognition and controlling problems with real-time images, and videos.


Riccardo Giubilato, Sebastiano Chiodini, Marco Pertile, Stefano Debei, 2006, An evaluation of ROS-compatible stereo visual SLAM methods on a nVidia Jetson TX2.

Artiom Basulto, Lantsova TecNM en Celaya Celaya, Mexico, 2003, Performance comparative of OpenCV Template Matching method on Jetson TX2 and Jetson Nano developer kits.

Linxiu Wu1, Houjie Li1,*, Jianjun He1 and Xuan Chen1, Traffic sign detection method based on Faster R-CNN, 2018.

Mogelmose A, Trivedi M M, Moeslund T B, Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(4), pp. 1484-1497.

Kamada H, Naoi S, Gotoh T, 2002, A compact navigation system using image processing and fuzzy control, Southeastcon ‘90. Proceedings. IEEE, vol. 1, 2002, pp. 337-342.

Janssen R, Ritter W, Stein F, et al, Hybrid Approach for Traffic Sign Recognition. Intelligent Vehicles ‘93 Symposium. IEEE, 2002, pp. 390-395.

Miura J, Kanda T, Shirai Y, An active vision system for real-time traffic sign recognition. Intelligent Transportation Systems. Proceedings. IEEE, 2002, pp. 52-57.

Maldonado-Bascon S, Lafuente-Arroyo S, Gil-Jimenez P, et al, Road-Sign Detection and Recognition Based on Support Vector Machines. IEEE Transactions on Intelligent Transportation Systems, 2007, 8(2), pp. 264-278.

Joseph Redmon Ali Farhadi, YOLOv3: An Incremental Improvement, 2018.

Meuter M, Nunn C, Gormer S M, et al, A Decision Fusion and Reasoning Module for a Traffic Sign Recognition System. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(4), pp. 1126-1134.

Álvaro Gonzalez, Garrido M Á, Llorca D F, et al, Automatic Traffic Signs and Panels Inspection System Using Computer Vision. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(2), pp. 485-499.

Creusen I I, Wijnhoven R R, Herbschleb E, et al, Color exploitation in HOG-based traffic sign detection, 2010.

Qian R, Zhang B, Yue Y, et al, Robust chinese traffic sign detection and recognition with deep convolutional neural network. International Conference on Natural Computation, IEEE, 2016, pp. 791-796.

Mukhometzianov R, Wang Y. Review, Machine learning techniques for traffic sign detection, 2017.

Shao-Kuo Tai, Christine Dewi, Rung-Ching Chen, Yan-Ting Liu, Xiaoyi Jiang and Hui YuDeep, Learning for Traffic Sign Recognition Based on Spatial Pyramid Pooling with Scale Analysis, 2020.

Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, University of Washington, 2012, Allen Institute for AI, Facebook AI Research You Only Look Once: Unified, Real-Time Object Detection.

J. Redmon. Darknet, 2016, Open source neural networks in c., 2013–2016.

Barnes N, Zelinsky A, Fletcher L S, Real-Time Speed Sign Detection Using the Radial Symmetry Detector, 2016. IEEE Press.

How to Cite
Nguyen Thi Nhung, Bui Thi Kim Thoa, Tran Long Quang Anh, & Nguyen Phu Dat. (2022). TRAFFIC SIGN DETECTION AND RECOGNITION WITH DEEP CONVOLUTIONAL NEURAL NETWORKS. UTEHY Journal of Science and Technology, 32, 39-45. Retrieved from