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  Published Paper Details:

  Paper Title

Elucidating Deep Learning Based Traffic Classification Using YOLO

  Authors

  T. Vasudeva Reddy,  A C S Srinivas,  Dr. M. Senthil Kumaran

  Keywords

Traffic Management, Yolo Algorithm, Signalized Traffic.

  Abstract


Intersections should have traffic signals for the purpose of ensuring safe driving. They disrupt and lessen traffic flow because of the queue delays at each traffic flow. In areas with high population densities, traffic congestion is a major challenge. Your location can be reached in a short while or up to an hour. Because to inefficient traffic management, which might be remedied much more swiftly in the technologically advanced world of today. For better traffic control, image processing techniques are developed as opposed to a time-based approach. Image processing enables us to more quickly clear traffic by calculating the number of vehicles in each traffic lane. This project makes use of the Yolo algorithm. The real-time characteristics of the traffic flows intended to cross the intersection at each traffic light are taken into account by this algorithm during the planning phase. The new algorithm will result in a decrease in traffic waiting time at intersections with signalized traffic, which will improve traffic flow. Also, more cars will be passing the crossing every second.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2305356

  Paper ID - 235835

  Page Number(s) - c711-c714

  Pubished in - Volume 11 | Issue 5 | May 2023

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  T. Vasudeva Reddy,  A C S Srinivas,  Dr. M. Senthil Kumaran,   "Elucidating Deep Learning Based Traffic Classification Using YOLO", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 5, pp.c711-c714, May 2023, Available at :http://www.ijcrt.org/papers/IJCRT2305356.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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