Drivers have profited significantly from developments in computer technology with the introduction of intelligent car systems. However, driver weariness is a key contributing cause to many car accidents. This research suggests an intelligent system for detecting driver tiredness based on vision, employing a convolutional neural network (CNN) to capture latent facial traits rather than relying on hand-engineered features like blink rate, eye closure, and yawning. The driver is classified as either sleepy or not by a softmax layer, which enables the system to alert the driver and avoid collisions. The proposed approach relies on extracting features from driver photos using a CNN and assessing eye states and facial expressions to reliably identify tiredness. The algorithm's excellent accuracy in identifying sleepy states is demonstrated by the results, underscoring its potential to improve road safety and lower the number of accidents caused by fatigued drivers.


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    Titel :

    Real-Time Driver Alertness Assessment Using Neural Networks


    Beteiligte:
    M, Yoga (Autor:in) / M, Ramyasri M (Autor:in) / R, Lokitha (Autor:in) / J, Jane Karunya (Autor:in) / N, Jeeva (Autor:in) / M, Kokila M (Autor:in)


    Erscheinungsdatum :

    20.01.2025


    Format / Umfang :

    554067 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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