Impact Factor
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Volume 12 Issue 07
July 2026
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Abstract
Driver Drowsiness Is One Of The Leading Causes Of Road Accidents Worldwide, Posing A Significant Threat To Human Life And Road Safety. This Paper Presents A Real-time Driver Drowsiness Detection System Employing Convolutional Neural Networks (CNNs) To Analyze Facial Cues Captured Via An In-vehicle Camera. The System Monitors Critical Fatigue Indicators Including Eye Closure Rate, Blink Frequency, Mouth State (yawning), And Head Orientation. A Modular Pipeline — Encompassing Image Acquisition, Face Detection Using Haar Cascades/SSD, Facial Landmark Extraction With OpenCV/Dlib, And CNN-based Drowsiness Classification — Enables Robust Real-time Inference. Upon Detecting Drowsiness, The System Triggers Multi-modal Alerts To Prompt Corrective Driver Action. Experimental Results Demonstrate High Detection Accuracy Across Varied Lighting And Environmental Conditions, Making The Proposed System A Viable Enhancement For Modern Vehicle Safety Systems.
Keywords
Paper ID
IJSARTV12I4105087
Publication Date
April 20, 2026
Research Area
Computer Applications