Research and Development of a Traffic Sign Recognition Module in Vietnam

Authors

  • Pham Xuan Tung University of Science and Technology of Hanoi, Vietnam | Vietnam Academy of Science and Technology, Vietnam
  • Nguyen Luong Thien Sefas Department, Space Technology Institute, Vietnam Academy of Science and Technology, Vietnam
  • Pham Van Bach Ngoc Sefas Department, Space Technology Institute, Vietnam Academy of Science and Technology, Vietnam https://orcid.org/0000-0002-7484-7402
  • Minh Hung Vu PetroVietnam University, Vietnam
Volume: 14 | Issue: 1 | Pages: 12740-12744 | February 2024 | https://doi.org/10.48084/etasr.6658

Abstract

Automatic traffic sign recognition is essential in researching and developing driver assistance systems and autonomous vehicles. This paper presents the research and development of an automated traffic sign recognition module in Vietnam. The recognition model is developed based on the deep learning model YOLOv5 and incorporates architectural modifications to reduce computational complexity, increase inference speed, and meet real-time requirements for embedded system applications. The model is trained using a custom dataset collected by the research team from real-world street environments in Vietnam, encompassing diverse locations, times, and weather conditions. The trained recognition model is deployed on the Jetson embedded system, yielding high-quality recognition results and meeting real-time recognition needs.

Keywords:

traffic sign recognition, YOLOv5, embedded system, image processing, deep learning

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How to Cite

[1]
P. X. Tung, N. L. Thien, P. V. B. Ngoc, and M. H. Vu, “Research and Development of a Traffic Sign Recognition Module in Vietnam”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 1, pp. 12740–12744, Feb. 2024.

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