Identifying the damage in a vehicle is considered to be the highest priority task in redeeming insurance from insurance industries. Whenever an accident occurs the guilty must be identified and the charges must be fined on him, these activities are actually carried out by the insurance companies. In this work, we present a different approach of identifying the vehicle’s damage by capturing 2D images, hence if the body of the vehicle gets deformed, the deprived driver of the vehicle need not have to wait for the insurance company’s consent and calculation of the ensured amount, instead the driver himself can calculate the recover amount from the insurance company. This work contains the CNN-based approach to check for the intensity and severity of damage. A computer vision approach is used accurately to classify damage of a vehicle and facilitates in claiming the right amount at right time.


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    Titel :

    CNN-Based Car Damage Detection


    Weitere Titelangaben:

    CogScienceTechnology


    Beteiligte:
    Kumar, Amit (Herausgeber:in) / Ghinea, Gheorghita (Herausgeber:in) / Merugu, Suresh (Herausgeber:in) / Hashimoto, Takako (Herausgeber:in) / Naganandini, G. (Autor:in) / Adarak, Shubhodeep (Autor:in) / Bagel, Prashanth (Autor:in)


    Erscheinungsdatum :

    2022-11-01


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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