Drowsy Driver Detection System using OpenCV and CNN . A real-time drowsiness detection system that alerts drivers when signs of drowsiness are detected using computer vision and deep learning. This project leverages OpenCV for video capture and CNN for eye state classification.

Developed a Driver Drowsiness Detection System using Raspberry Pi 4 and YOLO-based image processing to monitor eye state in real time. The system detects drowsiness by analyzing prolonged eye closure and triggers alerts to improve driver safety. Combined computer vision, deep learning, and embedded hardware into a cost-effective solution.

Sleep Detector is an AI-powered system that detects driver drowsiness in real time using deep learning and computer vision. It analyzes facial features from a webcam to classify drivers as drowsy or alert, helping prevent fatigue-related accidents. Built with Keras and TensorFlow, the model is trained on a labeled dataset for accurate detection.

Real-time Drowsiness Detection System using computer vision and deep learning. The system analyzes live camera feed for signs of drowsiness in drivers' eyes and triggers alarms

Real-time driver drowsiness detection system using computer vision and deep learning. Monitors eye movements through webcam feed and triggers alerts when drowsiness is detected. Built with Streamlit, TensorFlow, and OpenCV for easy web-based access and deployment.

Driver drowsiness detection system using deep learning to enhance road safety. Utilizes computer vision and a trained InceptionV3 model to monitor driver's eye state in real-time, alerting when signs of fatigue are detected.

A real-time, deep learning-based system designed to monitor and detect drowsiness in drivers using computer vision techniques. This project employs facial landmarks, eye state detection, gaze direction tracking, and head pose estimation to ensure safer driving by alerting drivers when drowsy or distracted.

No-Nap Drive is a real-time driver drowsiness detection system using computer vision and deep learning. It extracts Eye Aspect Ratio (EAR) via MediaPipe, analyzes temporal patterns using a 3-class LSTM model (Alert, Drowsy, Critical), and triggers severity-based alerts to help prevent microsleep-related accidents.

Real-time driver drowsiness detection system using computer vision, deep learning, and alert notifications via Twilio API.

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Real-time driver drowsiness detection system using computer vision and deep learning.

Deep learning based system for real-time driver drowsiness and distraction detection using computer vision.

Real-time AI driver drowsiness detection system using computer vision, deep learning, and multi-modal fatigue analysis.

Driver drowsiness and yawning detection system using computer vision and deep learning to improve road safety by alerting drivers in real time.

A real-time driver drowsiness detection system using computer vision and deep learning with IoT-based alerts for improved road safety.

Real-time driver drowsiness detection system using computer vision and deep learning (OpenCV, MediaPipe, Keras) with alert mechanism for road safety

Intelligent driver drowsiness detection system using computer vision and deep learning for real-time monitoring of eye closure, yawning, and cognitive distraction.

A real-time drowsiness detection system using computer vision and deep learning to monitor driver alertness by analyzing eye aspect ratio (EAR).

AI-Powered Real-Time Drowsiness Detection System Using Deep Learning and Computer Vision A real-time driver drowsiness detection system that leverages AI, deep learning (YOLOv8), and computer vision (Mediapipe & OpenCV) to monitor eye activity and alert users through audio-visual warnings when fatigue is detected.

An AI-based real-time driver drowsiness detection system using computer vision and deep learning to prevent road accidents by monitoring eye closure and alerting the driver.

Driver Drowsiness Detection This project provides a real-time driver drowsiness detection system using deep learning and computer vision. It monitors the driver’s eyes through a webcam and triggers an alarm if drowsiness is detected, helping to prevent accidents caused by fatigue.

A real-time driver drowsiness detection system using computer vision and deep learning, featuring a Next.js web dashboard and Android companion app for live monitoring and alerts.

This project is a real-time Driver Drowsiness Detection System designed to prevent accidents caused by driver fatigue. Using computer vision and deep learning, the system monitors the driver’s face and detects signs of drowsiness based on eye closure and head position.

This project develops a real-time driver drowsiness detection system using computer vision and deep learning. It monitors eye closure, yawning, and phone use, analyzes behavior over time, and gives alerts to improve driver safety.

Real-time drowsiness detection system using computer vision and deep learning. Monitors a driver’s eye movements to detect signs of fatigue and trigger alerts, improving road safety. Built with Python, OpenCV, and deep learning models.

AI-based Driver Drowsiness Detection System using Computer Vision and Deep Learning. It monitors eye movements and facial landmarks in real time to detect fatigue and alert the driver, enhancing road safety. Built with Python, OpenCV, and ML models for accurate, real-time detection.

A a real-time driver drowsiness detection system using computer vision and deep learning. This project was created as the Honours year project in the department of applied mathematics at Stellenbosch university.

😴 Drowsiness Detection System — A real-time alert system using computer vision to detect driver fatigue and prevent accidents. 🚗⚠️ Built with 🧠 OpenCV, Python, and Deep Learning • Created by Khyati Chahal 👩‍💻

Drowsiness Detection System A computer vision–based system that detects driver drowsiness in real time using OpenCV, Deep Learning, and Eye Aspect Ratio (EAR) techniques. The system alerts the driver when signs of sleepiness or eye closure are detected, helping prevent road accidents caused by fatigue.

A real-time Driver Drowsiness Detection system that uses computer vision and deep learning techniques to monitor eye movements and detect signs of fatigue. The system triggers alerts when drowsiness is detected, helping prevent accidents and improve road safety. Built using Python, OpenCV, and machine learning models.