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.
P. Priya, S. Kumar· International Scientific Jou...· 0 citations
Abstract: The AI-Powered Autonomous Multi-Functional Robot is an intelligent robotic system that integrates Artificial Intelligence (AI), Machine Learning (ML), Computer Vision, and the Internet of Things (IoT) to perform multiple tasks autonomously. The system is designed to reduce human intervention while improving operational efficiency, accuracy, and safety in dynamic environments. It uses advanced sensors and AI algorithms for real-time object detection, obstacle avoidance, autonomous navigation, and environmental monitoring. IoT connectivity enables remote monitoring, control, and cloud-based data management. The robot can be applied in industrial automation, healthcare, agriculture, smart homes, logistics, security surveillance, and disaster management. Its modular architecture ensures scalability and supports future enhancements with emerging technologies. Overall, the proposed system provides a reliable, cost-effective, and intelligent automation solution capable of addressing real-world challenges while contributing to the advancement of next-generation autonomous robotic systems.
Keywords: Artificial Intelligence (AI), Autonomous Robot, Machine Learning (ML), Computer Vision, Internet of Things (IoT), Autonomous Navigation, Object Detection, Obstacle Avoidance, Environmental Monitoring, Intelligent Decision-Making, Smart Automation, Industrial Robotics, Sensor Fusion, Cloud Connectivity, Real-Time Monitoring.
P. Priya, Paripalli Mahalakshmi· International Scientific Jou...· 0 citations