Fire Detection Model 🔥

This repository contains a real-time fire detection model based on the YOLO (You Only Look Once) architecture. The model is designed to identify fire outbreaks quickly and accurately, making it a critical tool for early fire detection and prevention.

Features

• Real-Time Detection: Identifies fire in live video streams or pre-recorded footage with high accuracy.
• YOLO Architecture: Leverages the power of YOLO for fast and efficient object detection.
• Customizable: Easily adaptable for different use cases by retraining the model with domain-specific datasets.
• Scalable: Suitable for implementation in drones, surveillance systems, or IoT devices.

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Getting Started

Prerequisites

Before using this repository, ensure you have the following installed:

• Python 3.7 or higher
• OpenCV
• PyTorch
• NumPy
• Matplotlib
• Other dependencies listed in requirements.txt

To install the required dependencies, run:

pip install -r requirements.txt

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Dataset Preparation

1. Collect Fire and Non-Fire Images: Gather a dataset of images containing fire and non-fire scenarios.
2. Annotate Data: Use tools like LabelImg to annotate the dataset with bounding boxes for fire regions.
3. Organize Data:
   • Place annotated images in the data/images folder.
   • Store annotation files in data/annotations.

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Training the Model

To train the YOLO-based fire detection model, follow these steps:

1. Prepare Configuration Files:

   • Adjust YOLO configuration files for your dataset (classes, input size, etc.).

2. Start Training:

   python train.py --data data/fire.yaml --cfg cfg/yolov5s.yaml --weights yolov5s.pt --epochs 50

3. Monitor Progress:

   • Training logs will display metrics like loss, mAP, and confidence thresholds.

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Testing the Model

Run the model on test images or videos to evaluate performance:

python detect.py --weights runs/train/exp/weights/best.pt --source data/test/video.mp4

Replace data/test/video.mp4 with the path to your test file.

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Deployment

To deploy the model in a real-time application:

1. Setup Camera Feed: Use OpenCV to capture video streams.
2. Integrate Model: Load the trained YOLO weights and process each frame for fire detection.
3. Trigger Alerts: Implement notifications or alarms when fire is detected.

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Project Structure

fire-detection-model/
│
├── data/
│   ├── images/            # Training and test images
│   ├── annotations/       # Annotation files
│   ├── test/              # Test videos or images
│
├── models/                # YOLO model configuration files
├── runs/                  # Trained models and results
├── utils/                 # Utility scripts for data preprocessing
│
├── train.py               # Training script
├── detect.py              # Inference script
├── requirements.txt       # Dependencies
├── README.md              # Project documentation

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Results

The model achieves high detection accuracy and low false positive rates on the test dataset. Below are some example outputs:

Metric	Value
Precision	95.2%
Recall	93.5%
[email protected]	94.7%

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Future Enhancements

• Support for multi-class fire detection (e.g., smoke, flame size, etc.).
• Integration with IoT devices for automated response systems.
• Optimization for edge computing devices.

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Contributing

Contributions are welcome! Feel free to submit issues or pull requests for enhancements, bug fixes, or additional features.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Acknowledgments

• YOLO for the detection framework.
• Open-source contributors for pre-trained weights and tools.