Driver Drowsiness Detection Based System and Alert System Using Deep Learning Models
TL;DR
This paper introduces a non-intrusive, real-time Driver Drowsiness Detection and Alert System utilizing computer vision and lightweight deep learning models, providing an affordable, robust solution to enhance smart vehicular safety.
Abstract
ABSTRACT: Driver drowsiness is one of the primary catalysts for highway accidents globally, resulting in significant loss of life and financial damage. Traditional fatigue detection systems often rely on invasive sensors or coarse vehicular telemetry that fail to capture sudden micro-sleep events. This paper introduces a non-intrusive, real-time Driver Drowsiness Detection and Alert System utilizing computer vision and lightweight deep learning models. The proposed system captures live video streams, extracts 68 facial landmark coordinates using dlib's specialized shape predictor, and calculates temporal changes in the Eye Aspect Ratio (EAR) to determine eye-closure rates. To handle varied environmental conditions and visual occlusions, a Convolutional Neural Network (CNN) is integrated to analyze spatial eye states. Upon identifying sustained eye closure exceeding a safe threshold, the system triggers localized audio alarms and dispatches emergency SMS alerts containing real-time GPS metadata via the Twilio API. Operating seamlessly on lightweight client environments, this system balances high predictive accuracy (96.4%) with low latency, providing an affordable, robust solution to enhance smart vehicular safety. Keywords: Driver Drowsiness Detection, Computer Vision, Deep Learning, Convolutional Neural Networks (CNN), Eye Aspect Ratio (EAR), dlib, OpenCV, Real-time Alert System, Smart Vehicle Safety, Twilio API.