Highway vehicle detection is essential for efficient traffic management. This project focuses on highway vehicle detection using advanced computer vision. The process of calculation and classification for creating a traffic survey report document, to do that manually in olden days, its spending more costly. There is a high possibility of errors in that calculation and classification of vehicles made by the surveyors. The study aims to propose an alternative method using deep learning concepts such as object detection, classification, tracking, and recognizing the vehicles. In this features of project can do detection of vehicles in roadways, addition to counting vehicles categorization like (car, truck, bicycle, motorbike, bus), estimation of the vehicle’s speed and vehicle traveling direction, and recognition of a vehicle’s approximate color and vehicle size. Automation of vehicles number plate recognition is done by (YOLOv8) object identification model. For removing foreground items from the background, we have used the MOG2 background subtraction technique. Backdrop is throughout a series of picture frames. The suggested framework able to recognize, categorize, and tally various kinds and sizes of vehicles as a plug-and-play apparatus. The suggested system has been tested at five sites with varying highways and weather circumstances.


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    Title :

    Reckoning and Speed Revealing of Automobile Using OpenCV and YOLOv8 by Real-Time Visual Image


    Additional title:

    stud. in Autonomic, Data-driven & Industrial Computing


    Contributors:

    Conference:

    International Conference on Computing and Communication Systems for Industrial Applications ; 2024 ; Delhi, India May 09, 2024 - May 10, 2024



    Publication date :

    2024-10-16


    Size :

    15 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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