Drowsiness Detection System

A computer vision-based drowsiness detection system that uses YOLO (You Only Look Once) object detection to identify drowsy and awake states in real-time video streams.

🚀 Features

• Real-time Detection: Monitor drowsiness levels in live video feeds
• YOLO-based Model: Uses Ultralytics YOLO for accurate object detection
• Multiple Input Sources: Support for webcam, video files, and images
• 17-Class Detection: Includes drowsy/awake states plus various other objects
• Cross-platform: Works on Windows, macOS, and Linux

📋 Requirements

System Requirements

• Python 3.8 or higher
• Webcam or video input device
• Sufficient RAM for real-time processing

Python Dependencies

pip install -r requirements.txt

Key dependencies include:

• Ultralytics (≥8.0.0) - YOLO model framework
• OpenCV (≥4.8.0) - Computer vision operations
• PyTorch (≥2.0.0) - Deep learning framework
• NumPy (≥1.26.0) - Numerical computations

🏗️ Project Structure

Drowsiness-Detection/
├── data/                   # Training and validation data
├── Detect/                 # Detection scripts
│   ├── detect_real_time.py # Real-time webcam detection
│   ├── detect_video.py     # Video file detection
│   └── img_detect.py       # Image detection
├── Scripts/                # Utility and data processing scripts
│   ├── collect_img_test.py # Test image collection
│   ├── validation.py       # Data validation
│   ├── check_data.py       # Data integrity checks
│   ├── cnt_img.py          # Image counting utility
│   └── cnt_labels.py       # Label counting utility
├── Test/                   # Test data and results
├── runs/                   # Training outputs and results
├── main.ipynb              # Main Jupyter notebook
├── data.yaml               # Dataset configuration
└── requirements.txt         # Python dependencies

🚀 Quick Start

1. Clone the Repository

git clone <repository-url>
cd Drowsiness-Detection

2. Install Dependencies

pip install -r requirements.txt

3. Run Real-time Detection

python Detect/detect_real_time.py

4. Test with Video File

python Detect/detect_video.py --source path/to/video.mp4

5. Analyze Single Image

python Detect/img_detect.py --source path/to/image.jpg

📊 Dataset Configuration

The system is configured for 17 classes as defined in data.yaml:

• Drowsiness Classes: awake, drowsy
• Other Objects: person, car, tv, cat, dog, and various food items

🔧 Usage Examples

Real-time Webcam Detection

from Detect.detect_real_time import detect_drowsiness

# Start real-time drowsiness detection
detect_drowsiness()

Video File Analysis

from Detect.detect_video import process_video

# Process video file for drowsiness detection
process_video("input_video.mp4", "output_video.mp4")

Image Analysis

from Detect.img_detect import detect_image

# Analyze single image
results = detect_image("test_image.jpg")

📈 Training and Model

The project uses YOLO models trained on custom drowsiness detection datasets. Training configurations and results are stored in the runs/ directory.

Training Data

• Training Images: data/images/
• Validation Images: data/images/
• Test Images: data/test/

🛠️ Development

Running Jupyter Notebook

jupyter notebook main.ipynb

Data Validation

python Scripts/validation.py
python Scripts/check_data.py

Data Statistics

python Scripts/cnt_img.py      # Count images
python Scripts/cnt_labels.py    # Count labels

📝 Notes

• The system requires a webcam for real-time detection
• Ensure sufficient lighting for optimal detection accuracy
• Model performance may vary based on hardware capabilities
• For best results, position the camera to capture clear facial features

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

📄 License

This project is open source. Please check the repository for specific licensing information.

🆘 Support

If you encounter any issues or have questions:

1. Check the existing documentation
2. Review the Jupyter notebook examples
3. Open an issue in the repository
4. Check the utility scripts in the Scripts/ folder for debugging

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Happy detecting! 🚀