A LIGHTWEIGHT CNN-BASED ANDROID VISION SYSTEM FOR REAL-TIME DRIVER DROWSINESS DETECTION USING EYE-STATE ANALYSIS

Abstract

Driver drowsiness remains one of the leading causes of road traffic accidents worldwide, as fatigue significantly impairs a driver's alertness, reaction time, and decision-making ability. Existing drowsiness detection approaches often require specialized hardware or computationally intensive models that limit their deployment on resource-constrained mobile devices. This study presents a lightweight CNN-based Android vision system for real-time driver drowsiness detection using eye-state analysis. The proposed system combines Haar Cascade classifiers for real-time face and eye localization with a custom Convolutional Neural Network (CNN) for classifying eye states as open or closed. A score-based decision mechanism continuously monitors eye closure across successive video frames and activates an audible alarm when prolonged eye closure indicates possible driver fatigue. The CNN model was trained using labeled eye-image datasets in Python with the Keras deep learning framework and subsequently integrated into an Android application for real-time inference using a smartphone camera. Experimental evaluation demonstrated that the developed system accurately classified eye states and generated timely drowsiness alerts under real-time operating conditions while maintaining computational efficiency suitable for mobile deployment. The findings demonstrate that lightweight deep learning models can be effectively deployed on Android devices to provide an accessible, low-cost, and practical solution for continuous driver drowsiness monitoring and improved road safety.