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Title	Deep Learning Based Smart Traffic Light System Using Image Processing With Yolo V7
ID_Doc	17852
Authors	Rangari A.P.; Chouthmol A.R.; Kadadas C.; Pal P.; Kumar Singh S.
Year	2022
Published	4th International Conference on Circuits, Control, Communication and Computing, I4C 2022
DOI	http://dx.doi.org/10.1109/I4C57141.2022.10057696
Abstract	India is home to 10% of all traffic deaths worldwide and has the second-largest road network in the world. Moreover, in smart cities, traffic congestion, pollutants, and noise pollution have increased due to a constant rise in vehicle kinds, technical problems with traffic signal management equipment, and inefficient road traffic management. Despite the fact that current traffic control systems rely on fixed time-based techniques, conventional traffic control systems are unable to manage the complicated traffic flow at junctions. Roadblocks increase mileage, increase transport costs, and pollute the air in addition to adding to the driver's stress and further delays. Therefore, we designed a smart traffic light management system employing the recently launched YOLO V7. The new version V7 of the YOLO algorithm outperforms all previous object detection models in both speed and accuracy. As it is the fastest and most accurate real-time object detection model hence it is the best algorithm to deploy in traffic controlling system. YOLO V7 is +120% faster than other previous models and shows the best speed to accuracy balance. © 2022 IEEE.
Author Keywords	CNN; Computer Vision; YOLO V7

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