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Smart city article details

Title	Optimizing Traffic Light Control Using Yolov8 For Real-Time Vehicle Detection And Traffic Density
ID_Doc	40922
Authors	Duc Q.-A.N.; Kim T.D.; Nguyen Q.-C.; Thi T.H.N.; Vu Q.; Xuan M.-D.D.; Nguyen V.-N.
Year	2024
Published	Proceedings of the 2024 9th International Conference on Integrated Circuits, Design, and Verification, ICDV 2024
DOI	http://dx.doi.org/10.1109/ICDV61346.2024.10616901
Abstract	The problem of traffic congestion becomes crucial as the number of vehicles on the roads, especially in big cities, continues to rise. An effective and cost-efficient solution is managing traffic lights based on density. This paper introduces a method utilizing deep neural networks to control traffic lights by analyzing surveillance camera footage and adjusting the lights automatically. Specifically, we employ the state-of-the-art YOLOv8 model to identify vehicles on the road from camera images, assess traffic density, and optimize the timing of traffic lights accordingly. The evaluation results show that the proposed model demonstrates a high accuracy in vehicle detection, achieving a mAP50 of up to \mathbf{9 7 \%}. In addition, the results of vehicle counting and traffic light control are also tested in various contexts. © 2024 IEEE.
Author Keywords	Deep learning; Smart city; Traffic congression; Traffic light; YOLO

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