#Driver Drowsiness Detection System

A real-time system designed to alert drivers when signs of drowsiness are detected, helping to reduce accidents caused by fatigue. This project uses Python, OpenCV, and Dlib for image processing and drowsiness detection.

Table of Contents

Overview Features Technologies Installation

Usage Project Structure

Limitations

Future Enhancements Contributors Overview The Driver Drowsiness Detection System is designed to improve road safety by monitoring the driver’s eye movements and alerting them if signs of drowsiness are detected. The system captures input from a webcam, analyzes eye blinks, and triggers an alarm if drowsiness is detected for over 5 seconds.

Features Real-Time Detection: Uses a webcam to monitor driver alertness. Alarm System: Triggers an alarm when drowsiness is detected. Threaded Alerts: Ensures continuous monitoring without lag. User-Friendly: Simple interface that runs smoothly on most systems.

technologies Python: Core programming language. OpenCV: For video capture and image processing. Dlib: For facial landmarks and eye detection. Threading: Used to handle alerts and prevent system lag. Installation

Clone this repository: bash Copy code git clone https://github.com/Hameeeeed/driver-drowsiness-detection.git Change to the project directory:

bash Copy code cd driver-drowsiness-detection Install the required packages: bash Copy code pip install -r requirements.txt

Usage Ensure a webcam is connected and positioned to capture the driver’s face. Run the main program:

bash Copy code python main.py

The system will begin monitoring and alerting as necessary.

Project Structure

bash Copy code driver-drowsiness-detection/

├── detection/
Detection and alert modules ├── main.py
Main script to run the detection system ├── requirements.txt
Python dependencies └── README.md
Project documentation

Limitations Lighting Conditions: Performance may degrade in low-light environments. Camera Position: Requires a direct view of the driver’s face for effective monitoring.

Future Enhancements Improved Accuracy: Explore advanced ML models to enhance detection. Vehicle Integration: Connect with vehicle systems for automated braking. Customization: Allow users to set alert sensitivity and alarm duration.