A real-time drowsiness detection system for drivers, which alerts the driver if they fall asleep due to fatigue while still driving. The computer vision algorithm used for the implementation uses a trifold approach to detect drowsiness, including the measurement of forward head tilt angle, measurement of eye aspect ratio (to detect closure of eyes) and measurement of mouth aspect ratio (to detect yawning).

This is my First project done by me and my friends in College First SEM.

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

A car safety technology that can auto-detect driver drowsiness in real-time. This system can prevent road accidents that are caused by drivers who fell asleep while driving.

基于Perclos＆改进YOLOv7的疲劳驾驶DMS检测系统（源码＆教程）

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The Drowsiness Detection System uses YOLOv8 models to monitor drowsiness in real-time by detecting eye states and yawning. Built with Python and leveraging the GroundingDINO library for bounding box generation, this project offers real-time alerts through a PyQt5 interface.

Driver drowsiness detection is a car safety Technology which helps prevent accidents caused by the driver getting drowsy. The following code uses computer vision to observe the driver's face, either using a built-in cameraor on mobile devices.

# Intellifotainment assist” – Smart HMI for passenger cars To run the program, download all files and save them in the same directory. After that, simply run 'Master.py' in the terminal. At the moment, the program will only run in linux based systems. # The Idea Infotainment systems have come a long way since the first set of dashboards installed in cars. Through our idea, we aim to create a Human Machine interaction model that takes infotainment systems to a new level. The driver tends to get distracted from the road while performing secondary tasks such as changing the music track, locking/unlocking the door while driving etc. Our system shall enable the driver to focus only on driving. Controlling the secondary tasks will be much easier. Our product primarily comprises of 5 modules: 1) Attention and drowsiness detection: - A camera shall be present on the dashboard, in front of the driver, behind the steering wheel. Through digital image processing techniques , using hough circle algorithm and haarcascade of an eye, we shall keep track of the driver’s sight. If he or she is looking away from the road while driving for more than a specified amount of time, we shall alert the driver to focus. We shall map the head orientation and iris position to accurately identify the driver’s attention. 2) Infotainment control features using blink combination: Through a combination of blinks, the driver can turn on or of the headlights, tail lights as well as indicators. Blinking of the eyes shall be detected using ‘dlib’ features in python. This shall give extremely accurate results. 3) Voice commands to control wipers, car lock, music system and windows A simple, yet extremely useful idea that would make the life of the driver a whole lot easy. Enabling the driver to speak to his car infotainment system would allow him to control and navigate these functionalities with great ease. The car will be enabled with a virtual assistant. 4) Automatic rear view mirror adjustment scheme: Using the camera placed in front of the driver, the system shall detect the position of the driver’s head. This shall also be done using image processing techniques and we shall identify the coordinates of the driver head in 3D space. There will be a mapping between the head position and mirror adjustment scheme. The mirrors will adjust their position using servo motors and shall do so automatically by identifying the head position. 5) A revolutionary reverse-assistance algorithm for smart parking and general reversing: Probably the highlight of our model, this feature shall make driving the car in and out of a parking spot, or rather, even reversing a car in general, far easier and safer than what it already is. Like most other modern cars, our model shall also have a camera installed at the back and the corresponding image displayed at the infotainment screen for parking assistance. Upon activating the reverse gear, the screen shall trace the line of motion of the car corresponding to the current position of the steering wheel. Because of this feature, the driver gets an idea of whether or not he’ll hit an obstacle while reversing if the steering wheel is kept at that position. Taking this feature to another level, the rear camera, after capturing the live video feed from the back of the car, shall perform image processing and machine learning algorithms to find a safe, obstacle-free path for reversing and indicate the driver to move the steering wheel accordingly. So instead of relying solely on the drivers judgement, our system shall actually find the path to be taken while reversing, such that other cars and other obstacles will be avoided, and accordingly recommend the driver to steer the wheel in that direction. This feature shall be extremely useful for new drivers/ learners. During the initial phase, to prevent errors from creeping in, we will always have a manual override button. After a good amount of testing, further modifications and refinements can be made. Our systems adds new dimensions to both precautionary safety measures, as well as convenience. If implemented properly, we are confident that our project will reach new heights of HMI and driver assistance technology. It will give drivers several less reasons to worry about.

CS-GY 6953 Deep Learning Major Project

A real-time drowsiness detection system using Python, OpenCV, and Dlib to monitor eye movements and detect signs of fatigue. When drowsiness is detected, it triggers an audio alert and sends an SMS notification using Twilio to a predefined contact.

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Driver Drowsiness Detection System

In this project, we have created a driver drowsiness detection system that will detect whether the driver's eyes are closed for too long and detect whether the driver is sleepy or inactive.

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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.

Driver Drowsiness Detection System for Road Safety

A real-time driver drowsiness detection system using Haar Cascade for face detection, LSTM for sequential analysis, and CNN for feature extraction, achieving 95.1% accuracy. The system monitors eye closure patterns and triggers alerts to prevent accidents and enhance road safety.

Driver Drowsiness Detection System with OpenCV & Keras

Driver drowsiness detection system using Computer Vision

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Every year many people lose their lives due to fatal road accidents around the world and Drowsiness and Fatigue of drivers are amongst the significant causes of road accidents. Alcohol, Overwork, Stress, and even Medical conditions can cause drivers to fall sleep. It is very important to detect the drowsiness of the driver to save life and property. So to reduce the accidents and save the life of a driver we propose to develop a system called as Driver Drowsiness Detection (D3 ) system. This system can automatically detect driver drowsiness in a real-time video stream and then play an alarm if the driver appears to be drowsy. Haar Cascade classifier, facial landmarks and computing Eye Aspect Ratio (EAR) to ensure proper detection of drowsiness in order to avoid accidents. For implementing this system we used libraries like Opencv and dlib.

An IOT cum Android based project to ensure the safety of car drivers by detecting drowsiness, detecting accident notifying all the nearby hospitals and gps tracking where owners can track their car even after being stolen through their app.

A tool to detect the driver face recognition and alert the driver with voice commands

System that can detect the drowsiness of drivers using CNN developed in Python - OpenCV, Keras

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Real-time AI Driver Drowsiness Detection using Flask, OpenCV & Dlib with live browser camera monitoring and fatigue alerts.

The majority of accidents happen due to the drowsiness of the driver. So, to prevent these accidents we will build a system using Python, OpenCV, and Keras which will alert the driver when he feels sleepy.

Driver Drowsiness Detection with YOLOv8 and Facial Features Combat driver fatigue with this deep learning-powered system that utilizes YOLOv8 to detect open and closed eyes, accurately assessing drowsiness levels.

I built a simple driver drowsiness detection system from scratch that uses computer vision to track blink counts and detect prolonged eye closure, triggering an alert!