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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

📞 Contact