Vehicles include cars, bikes, buses, aeroplanes, space shuttles, cycles and many more. Vehicle detection and vehicle-type recognition is a practical application of machine learning concepts and is straight away applicable for different tasks in a surveillance system of the traffic contributing to a smart surveillance. We will broach the functioning of instinctive vehicle-type identification using static image datasets. We have put forward Capsule Neural Networks for the identification of vehicle types by consolidating capsules to overcome drawbacks posed by CNN and its counterparts where CNN pooling loses significant data while modern processes can overcome this still image reconstruction is a tedious task. Capsule Network takes into different consideration delineations of object orientation, possie and relates its approach with inverse graphics making object recognition more efficient and accurate and significantly increasing classification performance.


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    Titel :

    Vehicle-Type Classification Using Capsule Neural Network


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Sharma, Harish (Herausgeber:in) / Shrivastava, Vivek (Herausgeber:in) / Kumari Bharti, Kusum (Herausgeber:in) / Wang, Lipo (Herausgeber:in) / Mane, Deepak (Autor:in) / Kharche, Chaitanya (Autor:in) / Bankar, Shweta (Autor:in) / Shinde, Swati V. (Autor:in) / Suryawanshi, Suraksha (Autor:in)


    Erscheinungsdatum :

    19.08.2022


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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