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Abstract

Driver drowsiness and fatigue are widely recognized as prevailing factors contributing to motor vehicle collisions. Annually, there is a significant increase in the number of deaths and fatalities due to a multitude of factors. The implementation of a driver warning system is imperative in order to mitigate traffic accidents, ultimately resulting in the preservation of human lives and public infrastructure. In this article, DDDS utilizing eye measure and head measure ensued developed as well as implemented. The DDDS was designed utilizing a high-resolution camera and employs Deep Cascaded Convolutional Neural Networks (DCCNN) to accurately identify instances of driver drowsiness. The Driver's Conduct Classification Neural Network (DCCNN) relies on the assessment of the driver's behavior through the analysis of visual cues, specifically eye movements, to determine whether the eyes are closed or open. The facial landmarks of the driver's frontal view were extracted using the Landmarks module from the Dlib toolkit. A novel parameter, referred to as "Eyes Aspect Ratio," has been identified through the analysis of landmarks associated with the eyes. The output of the Deep Cascaded Convolutional Neural Network (DCCNN) was subsequently employed to initiate a notification on the drive enclosure. In the experiment, an image with a resolution of 450320 pixels was utilized. The video is presented with a resolution of sixty frames per seconds (f/s). In relation to the accuracy of sleepiness detection, the present researchhas surpassed various previous studies

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