Driver fatigue is a leading cause of road accidents. This research presents a real-time driver drowsiness detection system using deep learning, optimized for low-power embedded systems. By compressing a complex model into a lightweight version, the system achieves an 85.5% accuracy in detecting various alertness states while maintaining energy efficiency. Utilizing facial landmark tracking and the Haar-Cascade method, the approach ensures fast and accurate detection, making it ideal for real-time vehicle safety applications. The system's integration into modern vehicles promises enhanced driver fatigue management and accident prevention.


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    Titel :

    Real-Time Driver Alertness Monitoring with Optimized Deep Learning and Haar-Cascade Methods


    Beteiligte:
    Rawat, Karan Singh (Autor:in) / Kumar, Shubham (Autor:in) / Kowsigan, M. (Autor:in) / S, Ramamoorthy (Autor:in) / P, Madhavan (Autor:in)


    Erscheinungsdatum :

    22.11.2024


    Format / Umfang :

    638716 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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