Phát triển hệ thống giám sát đèn giao thông bằng trí tuệ nhân tạo
,
,
,
và
Article Sidebar
Full Text: 
PDF
Abstract
The rapid increase in traffic volume, particularly personal vehicles, has created an urgent need for effective traffic light management and monitoring to ensure safety and reduce congestion. This study leverages artificial intelligence (AI) to develop a system capable of recognizing traffic light statuses (red, green, yellow, and malfunctioning) and transmitting data remotely, enabling timely detection and resolution of issues. The research process consists of two main phases: (1) data collection, processing, and training of the YOLOv8 model for traffic light recognition, and (2) development of an Android mobile application that connects users with the monitoring system. Experimental results demonstrate high model performance (mAP50 ~ 0.959), achieving an accuracy rate of 83% in traffic light recognition. This system not only enhances traffic monitoring through AI but also contributes to digital transformation efforts, paving the way for the development of intelligent transportation systems.
Tóm tắt
Sự gia tăng nhanh chóng của phương tiện giao thông, đặc biệt là xe cá nhân, đặt ra nhu cầu cấp bách về quản lý và giám sát tín hiệu đèn giao thông nhằm đảm bảo an toàn và giảm thiểu ùn tắc. Nghiên cứu này ứng dụng trí tuệ nhân tạo (AI) để phát triển một hệ thống nhận diện trạng thái đèn giao thông (đỏ, xanh, vàng, lỗi) và truyền dữ liệu từ xa, giúp phát hiện cũng như xử lý sự cố kịp thời. Quá trình nghiên cứu bao gồm hai giai đoạn chính: (1) thu thập, xử lý dữ liệu và huấn luyện mô hình YOLOv8 để nhận diện tín hiệu đèn giao thông, và (2) phát triển ứng dụng di động Android kết nối người dùng với hệ thống giám sát. Kết quả thực nghiệm cho thấy mô hình hoạt động với hiệu suất cao (mAP50 ~ 0.959), đạt độ chính xác nhận diện đèn giao thông lên đến 83%. Hệ thống này không chỉ nâng cao hiệu quả giám sát giao thông bằng AI mà còn góp phần thúc đẩy chuyển đổi số và hướng tới mô hình giao thông thông minh.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Tài liệu tham khảo
Ali, Y., Rafay, M., Khan, R. D. A., Sorn, M. K., & Jiang, H. (2023). Traffic problems in Dhaka City: causes, effects, and solutions (Case study to develop a business model). Open Access Library Journal, 10(5), 1-15. https://doi.org/10.4236/oalib.1109994
Almatar, K. M. (2024). Smart transportation planning and its challenges in the Kingdom of Saudi Arabia. Sustainable Futures, 8, 100238. https://doi.org/10.1016/j.sftr.2024.100238
Alomar, K., Aysel, H. I., & Cai, X. (2023). Data augmentation in classification and segmentation: A survey and new strategies. Journal of Imaging, 9(2), 46.
https://doi.org/10.3390/jimaging9020046
Bakirci, M. (2024). Real-time vehicle detection using YOLOv8-nano for intelligent transportation systems. Traitement du Signal, 41(4), 1727. https://doi.org/10.18280/ts.410407
Berhanu, Y., Alemayehu, E., & Schröder, D. (2023). Examining Car Accident Prediction Techniques and Road Traffic Congestion: A Comparative Analysis of Road Safety and Prevention of World Challenges in Low‐Income and High‐Income Countries. Journal of advanced transportation, 2023(1), 6643412. https://doi.org/10.1155/2023/6643412
Chen, X. (2022). Traffic lights detection method based on the improved yolov5 network. 2022 IEEE 4th International Conference on Civil Aviation Safety and Information Technology (ICCASIT), 1111-1114. https://doi.org/10.1109/ICCASIT55263.2022.9986726
Dikshit, S., Atiq, A., Shahid, M., Dwivedi, V., & Thusu, A. (2023). The use of artificial intelligence to optimize the routing of vehicles and reduce traffic congestion in urban areas. EAI Endorsed Transactions on Energy Web, 10. https://doi.org/10.4108/ew.4613
Ding, R.-X., Palomares, I., Wang, X., Yang, G.-R., Liu, B., Dong, Y., Herrera-Viedma, E., & Herrera, F. (2020). Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective. Information fusion, 59, 84-102. https://doi.org/10.1016/j.inffus.2020.01.006
Du, S., Pan, W., Li, N., Dai, S., Xu, B., Liu, H., Xu, C., & Li, X. (2024). TSD‐YOLO: Small traffic sign detection based on improved YOLO v8. IET Image Processing.
https://doi.org/10.1049/ipr2.13141
Duc, Q.-A. N., Kim, T. D., Nguyen, Q.-C., Thi, T. H. N., Vu, Q., Do Xuan, M.-D., & Nguyen, V.-N. (2024). Optimizing Traffic Light Control using YOLOv8 for Real-Time Vehicle Detection and Traffic Density. 2024 9th International Conference on Integrated Circuits, Design, and Verification (ICDV), 119-124. https://doi.org/10.1109/ICDV61346.2024.10616901
Duong, M.-T., Phan, T.-D., Nguyen, V.-B., & Le, M.-H. (2024). Identifying Traffic Congestion Through Vehicle Counting and Motion Estimation. International Conference on Green Technology and Sustainable Development, 124-137. https://doi.org/10.1007/978-3-031-76197-3_11
Flores-Calero, M., Astudillo, C. A., Guevara, D., Maza, J., Lita, B. S., Defaz, B., Ante, J. S., Zabala-Blanco, D., & Armingol Moreno, J. M. (2024). Traffic sign detection and recognition using YOLO object detection algorithm: A systematic review. Mathematics, 12(2), 297. https://doi.org/10.3390/math12020297
Greer, R., Gopalkrishnan, A., Landgren, J., Rakla, L., Gopalan, A., & Trivedi, M. (2023). Robust traffic light detection using salience-sensitive loss: Computational framework and evaluations. 2023 IEEE Intelligent Vehicles Symposium (IV), 1-7. https://doi.org/10.1109/IV55152.2023.10186624
Gupta, R., Singh, A. K., Dabral, S., & Tewari, P. P. (2023). IOT based traffic light control based on traffic density. 2023 Second International Conference on Informatics (ICI), 1-5. https://doi.org/10.1109/ICI60088.2023.10420867
Han, T., Sun, L., & Dong, Q. (2023). An improved YOLO model for traffic signs small target image detection. Applied Sciences, 13(15), 8754. https://doi.org/10.3390/app13158754
Hua, H. K., Nguyen, K. H., Quach, L.-D., & Tran, H. N. (2023). Traffic lights detection and recognition method using deep learning with improved YOLOv5 for autonomous vehicle in ROS2. Proceedings of the 2023 8th International Conference on Intelligent Information Technology, 117-122. https://doi.org/10.1145/3591569.3591589
Huu, P. N., Anh, B. N., & Minh, Q. T. (2024). Proposing Smart System for Detecting and Monitoring Vehicle Using Multiobject Multicamera Tracking. International Journal of Digital Multimedia Broadcasting, 2024(1), 6667738.
https://doi.org/10.1155/2024/6667738
Jutury, D., Kumar, N., Sachan, A., Daultani, Y., & Dhakad, N. (2023). Adaptive neuro-fuzzy enabled multi-mode traffic light control system for urban transport network. Applied Intelligence, 53(6), 7132-7153. https://doi.org/10.1007/s10489-022-03827-3
Khan, M., Raza, M. A., Abbas, G., Othmen, S., Yousef, A., & Jumani, T. A. (2024). Pothole detection for autonomous vehicles using deep learning: a robust and efficient solution. Frontiers in Built Environment, 9, 1323792. https://doi.org/10.3389/fbuil.2023.1323792
Krishna, S. S., Parisha, V., Varma, B. U. K., & Srinivasulu, C. (2024). Traffic Light Detection and Recognition using YOLOv8. 2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 1227-1233.
https://doi.org/10.1109/ICAAIC60222.2024.10575768
Kumar, T., Brennan, R., Mileo, A., & Bendechache, M. (2024). Image data augmentation approaches: A comprehensive survey and future directions. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3470122
Li, L. y., Yue, Q., & Luo, R. w. (2021). Road traffic sign recognition based on lightweight neural network. AOPC 2021: Optical Sensing and Imaging Technology, 12065, 393-398.
https://doi.org/10.1117/12.2605902
Luo, Y., Ci, Y., Jiang, S., & Wei, X. (2024). A novel lightweight real-time traffic sign detection method based on an embedded device and YOLOv8. Journal of Real-Time Image Processing, 21(2), 24.
https://doi.org/10.1007/s11554-023-01403-7
Musa, A. A., Malami, S. I., Alanazi, F., Ounaies, W., Alshammari, M., & Haruna, S. I. (2023). Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations. Sustainability, 15(13), 9859. https://doi.org/10.3390/su15139859
Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53, 102104. https://doi.org/10.1016/j.ijinfomgt.2020.102104
Philipsen, M. P., Jensen, M. B., Møgelmose, A., Moeslund, T. B., & Trivedi, M. M. (2015). Traffic light detection: A learning algorithm and evaluations on challenging dataset. 2015 IEEE 18th International Conference on Intelligent Transportation Systems, 2341-2345. https://doi.org/10.1109/ITSC.2015.378
Pillai, A. S. (2024). Traffic management: implementing AI to optimize traffic flow and reduce congestion. Journal of Emerging Technologies and Innovative Research, 11(7).
https://doi.org/10.2139/ssrn.4916398
Reis, D., Kupec, J., Hong, J., & Daoudi, A. (2023). Real-time flying object detection with YOLOv8. arXiv preprint arXiv:2305.09972. https://doi.org/10.48550/arXiv.2305.09972
Susilawati, H., Rahman, S. L. P., Rukmana, A., Matin, I. M. M., & Ismail, N. (2023). Smart Traffic Light Using Raspberry Pi and Digital Image Processing. 2023 9th International Conference on Wireless and Telematics (ICWT), 1-6.
https://doi.org/10.1109/ICWT58823.2023.10335398
Tran, X. K., Dinh, X. T., Pham, T. T., & Le, M. D. (2023). Research on computer vision techniques applied to vehicle tracking and real-time traffic monitoring. Journal of Science and Technology, 3(59), 20-24. https://doi.org/10.57001/huih5804.2023.103
Vietnam Goverment Portal. (2024). Law No. 36/2024/QH15 of the National Assembly: Law on road traffic order and safety.
https://chinhphu.vn/?pageid=27160&docid=211194&classid=1&orggroupid=1
Zhao, R., Tang, S. H., Shen, J., Supeni, E. E. B., & Rahim, S. A. (2024a). Enhancing autonomous driving safety: a robust traffic sign detection and recognition model TSD-YOLO. Signal Processing, 225, 109619.
https://doi.org/10.1016/j.sigpro.2024.109619
Zhao, Z., Wang, K., Wang, Y., & Liang, X. (2024b). Enhancing traffic signal control with composite deep intelligence. Expert Systems with Applications, 244, 123020. https://doi.org/10.1016/j.eswa.2023.123020
Zhou, L., Min, W., Lin, D., Han, Q., & Liu, R. (2020). Detecting motion blurred vehicle logo in IoV using filter-DeblurGAN and VL-YOLO. IEEE Transactions on Vehicular Technology, 69(4), 3604-3614. https://doi.org/10.1109/TVT.2020.2969427
Zhou, Q., Zhang, D., Liu, H., & He, Y. (2024). KCS-YOLO: An Improved Algorithm for Traffic Light Detection under Low Visibility Conditions. Machines, 12(8), 557.
https://doi.org/10.3390/machines12080557