Automation and improved traffic management are key problems for modern cities and regions. This article presents an approach to the recognition and analysis of traffic flow using the YOLOv8 object detection algorithm. The technique offers an effective solution for detecting vehicles and their movement characteristics. Based on neural networks and deep learning, YOLOv8 provides high accuracy and speed of data processing, which allows you to quickly and accurately determine the movement of cars, motorcycles, bicycles and other objects on the roads. The developed approach has the potential for application in various fields, including traffic flow management, road safety and the development of intelligent transport systems.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Video Image Recognition of Car Track Characteristics at Intersections


    Contributors:


    Publication date :

    2024-03-12


    Size :

    541383 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Design Elements at Cycle Track Intersections

    Edgar Bryant | Online Contents | 2016



    Detecting Cycle Failures at Signalized Intersections Using Video Image Processing

    National Research Council (U.S.) | British Library Conference Proceedings | 2005


    Application of Video Image Detection at Signalized Intersections During Reconstruction

    Surapaneni, R. / Courage, K. G. / Institute of Transportation Engineers | British Library Conference Proceedings | 1995


    Track Clearance Performance Measures for Railroad-Preempted Intersections

    Brennan, Thomas M. / Day, Christopher M. / Sturdevant, James R. et al. | Transportation Research Record | 2010