With the booming economy and better-than-ever road infrastructure, a growth in the count of motor vehicles moving on the road has been noticed. This increase in the number of vehicles may lead to various problems happening such as increased frequency of accidents, more violations in motor violations, and sometimes may lead to crimes as well. Hence, vehicle monitoring becomes a huge factor in overcoming these situations since manual monitoring of vehicles will become obsolete due to the large volume of vehicles as well as the high speed at which they travel. In this study, a project has been evolved for license plate identification using a Convolutional Neural Network (CNN) which happens to be a technique used for the deep analysis of algorithms. [11] An automatic license plate detection system based on YOLOv9 has been presented as a means of advancing the state-of-the-art in automatic license plate detection and contributing to the development of smarter, safer, and more sustainable urban environments.


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    Titel :

    Automatic License Plate Detection Using YOLOv9


    Beteiligte:
    V, Nivethitha (Autor:in) / Rajan, Shruthika (Autor:in) / Sriram, Suthir (Autor:in) / M, Thangavel (Autor:in)


    Erscheinungsdatum :

    15.11.2024


    Format / Umfang :

    965100 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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