Vehicle detection is an important method needed for autonomous administration of vehicle traffic control. It is the primary initial step often employed for operations like vehicle overspeed detection and other traffic movement violations. Monitoring vehicle activity is very important in crowded and widespread public roads of large organizations and institutions. It is important to employ efficient and optimized algorithms requiring low hardware resources and configurations. The software program must be fast and must work as a standalone system for consistency in its operation. YOLO algorithm is a lightweight method faster than most other CNN-based algorithms having acceptable performances for large objects. The paper used YOLO algorithm in MATLAB software for vehicle detection in a campus environment of Sikkim Manipal Institute of Technology (SMIT) campus. It was able to detect vehicles very effectively with an accuracy up to 95% with over 217 samples of training images.


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    Titel :

    Parameters Optimization of YOLO Algorithm for Vehicle Detection in SMIT Campus


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Dhar, Sourav (Herausgeber:in) / Do, Dinh-Thuan (Herausgeber:in) / Sur, Samarendra Nath (Herausgeber:in) / Liu, Chuan-Ming (Herausgeber:in) / Rai, Divya (Autor:in) / Rai, Bijay (Autor:in) / Chatterjee, Saikat (Autor:in)

    Kongress:

    International Conference on Communication, Devices and Networking ; 2022 ; Majhitar, India December 16, 2022 - December 17, 2022



    Erscheinungsdatum :

    2023-07-08


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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