Smart Fatigue Detection Model For Drivers

Authors: Shreyas Fatale, Ajinkya Bobade, Divya Jain, Kushal Agrawal, Prof. Mayur Chavan

Abstract: An abstract of this paper will provide a hybrid driver fatigue system that is supposed to be deployed in real-time on resource-constrained edge devices. The suggested framework integrates the lightweight Convolutional Neural Network (CNN) models with geometrical and temporal feature extraction based on the landmarks to provide a stable fatigue monitoring at the minimal computational complexity. The two parallel CNN models are used to perform eye state, and yawning detection, and geometric fatigue indicators are computed simultaneously through facial landmark Analysis. The percentage of eyelid closure (Eye Closure Percentage (PERC-LOS)) is used to determine eye fatigue and monitoring patterns of eyelid closure through time is an efficient measure of drowsiness, even in the process of lip detection in the presence of noise. The way of recognising yawning is the Jaw Drop Angle (JDA), which is a powerful geometric parameters calculated based on the nasal, chin, and jaw positions that is still reliable in spite of imprecision in lips location. A hybrid decision model combines deep learning-based predic-tions with geometric fatigue indicators to enhance the reliability of the system and help to reduce error. The general architecture proves to be practically viable as a cost-efficient approach to the intelligent driver assistance system, specifically in real-time embedded and edge computing applications.