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Title	Automated Traffic Management System Using Deep Learning Based Object Detection
ID_Doc	11266
Authors	Shankar P.; William P.; Tiwari S.S.; Maindola M.; Alabdeli H.; Yadav K.
Year	2024
Published	Proceedings of International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024
DOI	http://dx.doi.org/10.1109/ICSCAI61790.2024.10866932
Abstract	Despite having the second-largest road network in the world, India is responsible for ten percent of all deaths that occur on the world's roadways. In addition, there has been an increase in the amount of traffic congestion, air pollution, and noise pollution in smart cities as a result of the proliferation of various kinds of vehicles, the technical challenges associated with the technology used to operate traffic signals, and the poor management of road traffic. Even though they now rely on time-based methods, traditional traffic management systems are unable to handle the complicated flow of traffic that happens at intersections. This is the case even when these systems have been updated. Roadblocks may make travelling more stressful and cause further delays in addition to increasing the total number of miles driven, the cost of transportation, and the amount of pollution produced. We were able to accomplish this goal by developing a complete traffic light management system using the most recent version of YOLO, which was just recently published. In terms of both speed and accuracy, the most current iteration of the YOLO algorithm, which goes by the name V7, outperforms all of the prior object recognition models that have been developed. Because it is the fastest and most accurate real-time object identification model, it is the best way to use in traffic management systems. As a result, it is the best approach to use. Therefore, doing so is the most prudent thing to do in this situation. Yolo V7 is more than 120% faster than any of the previous incarnations, and it also has the highest speed to accuracy ratio of any of the versions. © 2024 IEEE.
Author Keywords	Deep Learning; Object Detection; speed detection; Traffic Management; vehicle detection; YOLO

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