SafeDriveVision is a computer vision project aimed at enhancing road safety. This project leverages deep learning models to detect and alert in real-time the dangerous behaviors of drivers, such as using a phone while driving or showing signs of drowsiness.

AI-powered Driver Drowsiness Detection System using Computer Vision & Machine Learning for real-time driver alertness monitoring and accident prevention.

A system which alarms the driver as soon as it detects that the driver is becoming drowsy to prevent any accident.

This is a system which can detect the drowsiness of the driver using CNN - Python, OpenCV

Driver Drowsiness Detection System

🚗 Driver Drowsiness Detection with Deep Learning (PoC) 🧠 A personal experiment comparing CNN + MobileNetV2 and YOLOv11 for real-time drowsiness detection. Focused on evaluating accuracy and efficiency to explore AI’s potential in preventing fatigue-related accidents. 😴⚡ A hands-on dive into safety-driven computer vision.

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Aware Driving (AD) is a mobile app that will assist you while you are driving.

Prototype of an intelligent safety system for detecting driver drowsiness

Automated Driver Drowsiness Control Technology Using Artificial Intelligence-based Decision Support System using Google Vision API.

In this project we will train our model on open and close eyes dataset then use that with face recognition library to check if the driver is sleeping or not.

Real-time driver drowsiness and distraction detection system. EU regulation C(2023)4523 compliant gaze zone detection.

Successfully established a deep learning model which can accurately predict the drowsiness state of an individual and thereby alert a driver who is in a drowsy state for preventing fatal accidents.

Computer Vision model to detect eyes and alert when the user is drowsy.

Drowsiness Detection

Driver drowsiness detection with face recognition.

Embeded System Project - Driver Drowsiness Detection

Project topic is DrowsiShield that ensures drivers safety. The main aim of this system is to avoid accidents by instantly detecting the driver’s level of drowsiness and sending alerts through audio and haptic stimulation.

A Computer-vision based project to detect a driver’s drowsiness based on facial features using Python.

This is an Android Application made to alert the passengers in a vehicle if their driver is sleeping or is in a state of drowsiness.

Real-time driver drowsiness detection system using OpenCV and MediaPipe that monitors eye aspect ratio and triggers alerts when fatigue is detected.

This model can predict the drowsiness of a driver.

This Python project detects driver drowsiness in real time using eye aspect ratio (EAR) from facial landmarks. If eyes stay closed beyond a threshold, it triggers an alert sound. Built with OpenCV, dlib, and pygame, it enhances road safety by monitoring driver alertness via webcam.

Real-time Driver Drowsiness Detection using Machine Learning and Deep Learning.

🚨 Detect accidents in images using AI and initiate emergency responses for smarter city and traffic management.

Django Webapp for Driver Drowsiness Detection

Driver Drowsiness Detection with Deep learning and Yolo

Real-time driver drowsiness detection system using computer vision, eye-blink analysis, OpenCV, and Dlib.

A real-time drowsiness detection system built with OpenCV, dlib, and deep learning landmarks, designed to enhance driver safety by monitoring fatigue.

Detecting Driver Drowsiness using OpenCV and TensorFlow