Driver Fatigue Detection Model

📝 Overview

The Driver Fatigue Detection Model is an AI-powered system designed to detect driver fatigue through real-time facial feature analysis. By monitoring signs such as eye movement and yawning, the model aims to reduce road accidents caused by driver exhaustion.

📂 Project Structure

• Dataset: Images of drivers annotated with fatigue levels based on key facial attributes.
• Model: Deep learning (CNN-based) models to classify and detect fatigue signs.
• Real-Time Detection: Integrated camera support for live monitoring and alerts.
• Evaluation: Assesses model accuracy and responsiveness under real-world scenarios.

🚀 Key Features

1. Real-time facial analysis for fatigue detection.
2. Automated alerts to warn drivers.
3. Adjustable sensitivity thresholds for fatigue detection.
4. User-friendly interface.

⚙️ System Requirements

• Programming Language: Python 3.7+
• Libraries: OpenCV, TensorFlow/Keras, NumPy, Matplotlib
• Hardware: HD camera, GPU support for TensorFlow (for real-time performance).

License

This project is licensed under the MIT License.
You are free to use, modify, and distribute this project under the terms of the license.

🌟 Installation

1. Clone the repository:

   git clone https://github.com/Mr-1504/Driver-Fatigue-Detection-Model.git
   cd Driver-Fatigue-Detection-Model

2. Install required dependencies:

   pip install -r requirements.txt

3. Run the model:

   python main.py

🔧 Contribution

1. Fork this repository.
2. Create a feature branch:

   git checkout -b feature-branch-name

3. Commit your changes:

   git commit -m "Add new feature"

4. Submit a pull request for review.

📖 References

• OpenCV Documentation
• TensorFlow Documentation

📞 Contact

• Author: Mr-1504
• Email: [email protected]