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Deep Learning Architecture for a Real-Time Driver Safety Drowsiness Detection System
Abstract
According to the reports from the World Health Organization (WHO), one of the primary causes that led to death in the world was road accidents. Every year, numerous road accidents are caused by drivers due to their drowsiness. It can be minimized by alerting the driver, and it has been done by identifying and recognizing the initial stages of drowsiness. Several models have been proposed to detect drivers' drowsiness and alert them before a road accident occurs. However, the most prominent one is VGG16 with a transfer learning mechanism that is utilized to view the status of the respective regions of interest. By utilizing these models, the drivers are monitored, and alarms are generated to alert the drivers as well as the passengers. This experimental analysis was carried out on the Kaggle Yawn-Eye-Dataset (KYED), and the results showed the low computational intricacy and high precision of the eye closure estimation and the ability of the proposed system for drowsiness detection.
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