Drowsy driving is a leading cause of road accidents, resulting in significant injuries, fatalities, and economic losses annually. To tackle this issue, we introduce NoDoze, a real-time drowsiness detection system designed to enhance road safety through continuous, automated, and personalized monitoring of driver alertness. NoDoze utilizes advanced computer vision to track facial cues such as eye closure, yawning, and head tilts—key indicators of fatigue. Machine learning algorithms process these cues in real-time, enabling highly accurate drowsiness detection. Upon identifying fatigue, the system triggers audible alerts to reengage the driver and prevent accidents. It also includes an emergency response feature that shares the driver’s real-time location with predefined contacts for timely assistance. Built for accessibility, NoDoze operates on lightweight frame-works compatible with in-car devices and smartphones, requiring no complex hardware. Rigorous testing across diverse conditions has demonstrated its reliability, adaptability, and robustness, with consistent performance in varying lighting, driver profiles, and environments.NoDoze represents a significant step forward in proactive road safety, combining advanced technology with practical implementation to reduce drowsy driving incidents. By providing an effective real-time detection tool, NoDoze has the potential to save lives, prevent injuries, and improve road safety.


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    Title :

    Nodoze: Real Time Drowsiness Detection System


    Contributors:


    Publication date :

    2025-04-28


    Size :

    356149 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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