fire-detection

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🔥 Real-time fire detection using YOLOv8 🚒📸 for accurate image-based fire recognition with Python.

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# 🔥 Real-Time Fire Detection System

[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://deepukr0315-fire-detection-app-qrxz8o.streamlit.app/)

A real-time fire detection system powered by YOLOv8 and Streamlit that can detect fire through your webcam with high accuracy and low latency.

## 🌟 Features

- **Real-time Detection**: Process webcam feed in real-time at ~30 FPS
- **High Accuracy**: Custom-trained YOLOv8 model for fire detection
- **User-Friendly Interface**: Clean, intuitive Streamlit web interface
- **Live Statistics**: Real-time FPS, frame count, and detection metrics
- **Confidence Scoring**: Visual progress bar showing detection confidence
- **Responsive Design**: Works on desktop and mobile devices

## 🚀 Live Demo

Try the application live: **[Fire Detection App](https://deepukr0315-fire-detection-app-qrxz8o.streamlit.app/)**

> **Note**: The live demo requires webcam access. Make sure to allow camera permissions when prompted.

## 📊 Model Performance

- **Model**: Custom YOLOv8s trained for fire detection
- **Training Dataset**: Fire detection dataset with 4 versions
- **Training Epochs**: 80 epochs
- **Input Size**: 640x640 pixels
- **Confidence Threshold**: 60%
- **Detection Classes**: Fire objects with bounding boxes

## 🛠️ Installation

### Prerequisites

- Python 3.8+
- Webcam/Camera access
- Modern web browser

### Local Installation

1. **Clone the repository**
   ```bash
   git clone https://github.com/your-username/fire-detection.git
   cd fire-detection
   ```

2. **Create virtual environment**
   ```bash
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   ```

3. **Install dependencies**
   ```bash
   pip install -r requirements.txt
   ```

4. **Add your model file**
   - Place your trained `best.pt` model file in the root directory
   - Or update the `.gitignore` to include your model file

5. **Run the application**
   ```bash
   streamlit run app.py
   ```

## 📁 Project Structure

```
fire-detection/
├── app.py                          # Main Streamlit application
├── best.pt                         # Trained YOLO model (not in repo)
├── requirements.txt                # Python dependencies
├── fire-detection.ipynb            # Training notebook
├── main.py                         # Model inference script
├── .gitignore                      # Git ignore file
├── README.md                       # Project documentation
└── test_setup.py                   # Setup verification script
```

## 🔧 Configuration

### Model Configuration
- **Confidence Threshold**: Adjust in `app.py` (default: 0.6)
- **Image Size**: 640x640 pixels (recommended for speed/accuracy balance)
- **FPS Limit**: Configurable delay in detection loop

### Camera Settings
- **Resolution**: 640x480 (optimized for performance)
- **FPS**: 30 FPS target
- **Auto-detection**: Automatically detects available camera devices

## 📋 Requirements

```txt
streamlit
ultralytics
torch
torchvision
opencv-python-headless>=4.8.0
numpy
pandas
pillow
```

## 🎯 How It Works

1. **Model Loading**: Loads the custom-trained YOLOv8 fire detection model
2. **Camera Access**: Initializes webcam feed with optimal settings
3. **Real-time Processing**: Processes each frame through the YOLO model
4. **Detection Analysis**: Identifies fire objects with confidence scores
5. **Visual Feedback**: Displays bounding boxes and alerts when fire is detected
6. **Statistics Tracking**: Monitors performance metrics and detection counts

## 🔥 Model Training Details

The fire detection model was trained using:

- **Base Model**: YOLOv8s (Small variant for speed)
- **Dataset**: Fire Detection Dataset v4 from Roboflow
- **Training Environment**: Google Colab with GPU acceleration
- **Optimization**: 80 epochs with early stopping
- **Validation**: Comprehensive validation on test dataset

### Training Process
```python
# Training command used
!yolo task=detect mode=train model=yolov8s.pt data=data.yaml epochs=80 imgsz=640 plots=True
```

## 🚀 Deployment

### Streamlit Cloud
The application is deployed on Streamlit Cloud with automatic deployments from the main branch.

### Local Deployment
```bash
# Install and run locally
pip install -r requirements.txt
streamlit run app.py
```

### Docker Deployment
```dockerfile
# Dockerfile example
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8501
CMD ["streamlit", "run", "app.py"]
```

## 🔍 Troubleshooting

### Common Issues

1. **Model File Not Found**
   - Ensure `best.pt` is in the root directory
   - Check `.gitignore` settings if using version control

2. **Camera Access Denied**
   - Allow camera permissions in your browser
   - Check if another application is using the camera

3. **Performance Issues**
   - Reduce confidence threshold for faster processing
   - Close other applications using camera/GPU resources

4. **Import Errors**
   - Verify all dependencies are installed: `pip install -r requirements.txt`
   - Check Python version compatibility (3.8+)

### Debug Mode
Run the test script to verify your setup:
```bash
python test_setup.py
```

## 📈 Performance Metrics

- **Inference Speed**: ~30 FPS on modern hardware
- **Memory Usage**: ~500MB RAM (with model loaded)
- **CPU Usage**: 15-30% (depends on hardware)
- **Latency**: <100ms detection latency

## 🤝 Contributing

1. Fork the repository
2. Create a feature branch (`git checkout -b feature/improvement`)
3. Commit your changes (`git commit -am 'Add new feature'`)
4. Push to the branch (`git push origin feature/improvement`)
5. Create a Pull Request

## 📝 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- **Ultralytics**: For the amazing YOLOv8 framework
- **Streamlit**: For the fantastic web app framework  
- **Roboflow**: For providing the fire detection dataset
- **OpenCV**: For computer vision capabilities

## 📞 Contact

- **Author**: Your Name
- **Email**: your.email@example.com
- **LinkedIn**: [Your LinkedIn Profile](https://linkedin.com/in/yourprofile)
- **GitHub**: [Your GitHub Profile](https://github.com/yourusername)

## 🚨 Important Notes

- This system is designed for demonstration purposes
- For production fire safety systems, additional testing and validation are required
- Always have proper fire safety equipment and procedures in place
- This tool should complement, not replace, professional fire detection systems

---

**⭐ If you found this project helpful, please give it a star!** ⭐

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