Automated traffic monitoring is an essential system in our daily life offering numerous benefits. It helps drivers stay aware of their speed, reducing the risk of accidents and saving lives, while also aiding law enforcement in effectively regulating traffic. The goal of this research aims to compare the evaluation of the car detection while estimating the speed of both Haar Cascade and Yolov8 methods. Experimental results demonstrated that our proposed one outperformed with the accuracy MAE is about 0.77, along with the precision 93% (96.7% for actual car detection) in the same testing data, in contrast Haar Cascade is just around 62% (67.3% for actual detection), MAE at 4.27.


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    Titel :

    YOLOv8 for Vehicle Detection and Speed Estimation


    Weitere Titelangaben:

    Lect. Notes on Data Eng. and Comms.Technol.


    Beteiligte:
    Hassanien, Aboul Ella (Herausgeber:in) / Rizk, Rawya Y. (Herausgeber:in) / Darwish, Ashraf (Herausgeber:in) / Alshurideh, Muhammad Turki Raji (Herausgeber:in) / Snášel, Vaclav (Herausgeber:in) / Tolba, Mohammed F. (Herausgeber:in) / Do, Anh Kim Thi (Autor:in) / Le Truong, Thanh My (Autor:in) / Debnath, Narayan C. (Autor:in) / Nguyen, Vinh Dinh (Autor:in)

    Kongress:

    International Conference on Advanced Intelligent Systems and Informatics ; 2025 ; Port Said, Egypt January 19, 2025 - January 21, 2025



    Erscheinungsdatum :

    21.02.2025


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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