Drowsiness Detector using Python. College Project with all Documents

Driver Drowsiness Detection System for Road Safety

Apply deeplearning for detecting and warning of driver drowsiness

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.

Python project on driver drowsiness detection system. Uses OpenCV and keras to detect drowsiness by analyzing eyes of user. Custom model has been trained on dataset of open and closed eyes of humans. Webapp has been built using Django and Api is built using flask framework.

Flask Integrated Classifier Model for Closed or Open Eye Detection

This is our Sem 5 mini project. It is about driver drowsiness. We measure if they are sleepy, drowsy or active(awake).

Real-Time Drowsiness Identification based on Eye State Analysis

DriveSafe AI is a real-time driver drowsiness detection system that uses Python and OpenCV to monitor eye movements through a webcam. The system detects fatigue based on eye closure patterns and provides instant audio and visual alerts using a web-based interface.

The Drowsiness Detection System is an intelligent safety application designed to reduce road accidents caused by driver fatigue. Using computer vision, the system employs a Convolutional Neural Network (CNN) trained to recognize facial features, specifically focusing on the eyes.

IoT-based system for real-time driver drowsiness detection using ESP32 and cloud services for monitoring and alerts.

Very Simple Python-based drowsiness detection system by utilizing computer vision and facial landmarks to enhance road safety.

This project is made by me as our data science project

Drowsy Driver Detection

A real-time computer vision system that detects driver drowsiness using Python, Mediapipe, and the Eye Aspect Ratio (EAR) technique.

It is machine learning based model used for detecting the drowsiness of a person. Can be used in vehicles.

A Python script that checks driver attentiveness using machine learning computer vision.

Real-time driver drowsiness detection using MediaPipe FaceMesh + CNN/BiLSTM hybrid. Detects eye closure, yawning and head pose via facial landmarks with personal calibration and audible alerts.

This project is a Driver Monitoring System that detects drowsiness and mobile phone usage using YOLOv8, OpenCV, and Dlib. The system continuously monitors the driver's facial expressions and actions to ensure road safety.