🔥 Fire Detection System - AI-Powered Wildfire Detection

An intelligent real-time wildfire detection system powered by YOLOv8 deep learning model. Detect fires in images and video streams with high accuracy and automated alert capabilities.

Real-time fire detection in action

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🌟 Key Features

🎯 Detection Capabilities Real-time Fire Detection using YOLOv8 Multi-source Input Support Images (JPG, PNG, etc.) Video files (MP4, AVI, etc.) Live camera streams High Accuracy (92%+ mAP@0.5) Fast Inference (<100ms per frame)

🚀 Production Features REST API with FastAPI Interactive Web UI for testing Automated Alerts (Email/SMS) Confidence Scoring & bounding boxes Batch Processing support Docker Deployment ready

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📊 Performance Metrics

Metric	Value	Hardware
mAP@0.5	92.3%	-
Precision	89.4%	-
Recall	91.2%	-
Inference Time	45ms	GPU (RTX 3090)
Inference Time	180ms	CPU (i7-12700)
FPS	125	GPU
Model Size	6.2 MB	YOLOv8n

---

🏗️ System Architecture

┌─────────────────────────┐
│   Input Sources         │
│  (Image/Video/Camera)   │
└───────────┬─────────────┘
            │
            ▼
┌─────────────────────────┐
│   Preprocessing         │
│  (Resize, Normalize)    │
└───────────┬─────────────┘
            │
            ▼
┌─────────────────────────┐
│   YOLOv8 Model          │
│  (Fire Detection)       │
└───────────┬─────────────┘
            │
            ▼
┌─────────────────────────┐
│   Post-processing       │
│  (NMS, Filtering)       │
└───────────┬─────────────┘
            │
            ▼
┌─────────────────────────┐
│   FastAPI Backend       │
│  (REST API Endpoints)   │
└───────────┬─────────────┘
            │
            ▼
┌─────────────────────────┐
│   Alert System          │
│  (Email/SMS/Dashboard)  │
└─────────────────────────┘

---

🚀 Quick Start

Prerequisites

• Python 3.9 or higher
• pip package manager
• (Optional) CUDA-capable GPU for faster inference
• (Optional) Webcam for live detection

Installation

1. Clone the repository

   git clone https://github.com/SalimTag/firedetection.git
   cd firedetection

2. Install dependencies

   pip install -r requirements.txt

3. Download the trained model (if not included)

   # Option 1: Download from releases
   wget https://github.com/SalimTag/firedetection/releases/download/v1.0/fire_yolov8.pt -P models/
   
   # Option 2: Train your own model (see Training section)

4. Configure environment variables

   cp .env.example .env
   # Edit .env with your configuration

Running the Application

Option 1: Start FastAPI Backend

uvicorn main:app --reload --host 0.0.0.0 --port 8000

Access the API:

• API Server: http://localhost:8000
• Interactive Docs: http://localhost:8000/docs
• Alternative Docs: http://localhost:8000/redoc

Option 2: Run Inference Directly

from ultralytics import YOLO

# Load model
model = YOLO('models/fire_yolov8.pt')

# Run inference
results = model('path/to/image.jpg')

# Display results
results[0].show()

---

💻 Usage Examples

1. REST API

Detect fire in an image:

curl -X POST "http://localhost:8000/api/detect" \
  -F "file=@forest_fire.jpg"

Response:

{
  "detections": [
    {
      "class": "fire",
      "confidence": 0.87,
      "bbox": [120, 45, 340, 280]
    }
  ],
  "processing_time_ms": 45,
  "alert_sent": true
}

2. Python Integration

Basic detection:

import requests

# Upload and detect
with open('image.jpg', 'rb') as f:
    response = requests.post(
        'http://localhost:8000/api/detect',
        files={'file': f}
    )

result = response.json()
print(f"Found {len(result['detections'])} fire instances")
print(f"Confidence: {result['detections'][0]['confidence']:.2%}")

Live camera feed:

import cv2
import requests
from io import BytesIO

cap = cv2.VideoCapture(0)  # 0 for webcam

while True:
    ret, frame = cap.read()
    if not ret:
        break
    
    # Encode frame
    _, buffer = cv2.imencode('.jpg', frame)
    img_bytes = BytesIO(buffer)
    
    # Send to API
    response = requests.post(
        'http://localhost:8000/api/detect',
        files={'file': ('frame.jpg', img_bytes, 'image/jpeg')}
    )
    
    detections = response.json()['detections']
    
    # Draw bounding boxes
    for det in detections:
        x1, y1, x2, y2 = det['bbox']
        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
        label = f"Fire {det['confidence']:.2f}"
        cv2.putText(frame, label, (x1, y1-10), 
                   cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
    
    cv2.imshow('Fire Detection', frame)
    
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

3. Video Processing

from ultralytics import YOLO

model = YOLO('models/fire_yolov8.pt')

# Process video
results = model('forest_surveillance.mp4', save=True)

# Results saved to runs/detect/predict/

---

📁 Project Structure

firedetection/
├── backend/
│   ├── main.py                    # FastAPI application entry point
│   ├── routers/
│   │   ├── detection.py           # Detection endpoints
│   │   └── alerts.py              # Alert management
│   ├── services/
│   │   ├── detector.py            # YOLOv8 detection service
│   │   ├── alert_service.py       # Notification service
│   │   └── video_processor.py     # Video stream processing
│   ├── models/
│   │   └── fire_yolov8.pt         # Trained model weights
│   ├── utils/
│   │   ├── image_utils.py         # Image preprocessing
│   │   └── visualization.py       # Result visualization
│   └── requirements.txt           # Python dependencies
├── frontend/                      # (Optional) Web interface
├── notebooks/
│   ├── training.ipynb             # Model training notebook
│   └── evaluation.ipynb           # Performance evaluation
├── docs/
│   ├── demo.gif                   # Demo animation
│   └── screenshots/               # UI screenshots
├── tests/
│   └── test_detector.py           # Unit tests
├── .env.example                   # Environment variables template
├── .gitignore
├── docker-compose.yml             # Docker deployment
├── Dockerfile
├── LICENSE
└── README.md

---

🎓 Model Training

Dataset Preparation

1. Organize your dataset:

   dataset/
   ├── images/
   │   ├── train/
   │   ├── val/
   │   └── test/
   └── labels/
       ├── train/
       ├── val/
       └── test/
   

2. Create dataset configuration (fire_dataset.yaml):

   path: ./dataset
   train: images/train
   val: images/val
   test: images/test
   
   nc: 1  # number of classes
   names: ['fire']

Training the Model

from ultralytics import YOLO

# Load pretrained model
model = YOLO('yolov8n.pt')  # nano (fastest)
# or model = YOLO('yolov8m.pt')  # medium (more accurate)

# Train
results = model.train(
    data='fire_dataset.yaml',
    epochs=100,
    imgsz=640,
    batch=16,
    device=0,  # GPU device (or 'cpu')
    patience=20,
    save=True,
    name='fire_detection'
)

# Evaluate
metrics = model.val()
print(f"mAP@0.5: {metrics.box.map50}")
print(f"mAP@0.5:0.95: {metrics.box.map}")

# Export (optional)
model.export(format='onnx')  # for deployment

Dataset Augmentation

The dataset preparation code is available in the separate repository: 📦 Fire-Detection Repository

Features:

• Automated data augmentation (rotation, flip, brightness, etc.)
• Dataset splitting utilities
• Label format conversion
• Data quality validation

---

🐳 Docker Deployment

Using Docker Compose (Recommended)

# Build and start services
docker-compose up -d

# View logs
docker-compose logs -f

# Stop services
docker-compose down

Manual Docker Build

# Build image
docker build -t firedetection:latest .

# Run container
docker run -p 8000:8000 firedetection:latest

docker-compose.yml:

version: '3.8'

services:
  api:
    build: .
    ports:
      - "8000:8000"
    environment:
      - MODEL_PATH=/app/models/fire_yolov8.pt
      - CONFIDENCE_THRESHOLD=0.5
    volumes:
      - ./models:/app/models
    restart: unless-stopped

---

⚙️ Configuration

Create a .env file in the project root:

# Model Configuration
MODEL_PATH=models/fire_yolov8.pt
CONFIDENCE_THRESHOLD=0.5
IOU_THRESHOLD=0.45

# API Configuration
API_HOST=0.0.0.0
API_PORT=8000
CORS_ORIGINS=*

# Alert Configuration
ENABLE_ALERTS=true
ALERT_EMAIL=alerts@example.com

# SMTP Settings (for email alerts)
SMTP_SERVER=smtp.gmail.com
SMTP_PORT=587
SMTP_USERNAME=your-email@gmail.com
SMTP_PASSWORD=your-app-password

# Optional: Twilio SMS Alerts
TWILIO_ACCOUNT_SID=your-account-sid
TWILIO_AUTH_TOKEN=your-auth-token
TWILIO_PHONE_NUMBER=+1234567890
ALERT_PHONE_NUMBER=+1234567890

---

🧪 Testing

Run Tests

# Install test dependencies
pip install pytest pytest-cov

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=. --cov-report=html

Example Test

# tests/test_detector.py
import pytest
from services.detector import FireDetector
import numpy as np

def test_detector_initialization():
    detector = FireDetector('models/fire_yolov8.pt')
    assert detector.confidence_threshold == 0.5

def test_fire_detection():
    detector = FireDetector('models/fire_yolov8.pt')
    test_image = np.zeros((640, 640, 3), dtype=np.uint8)
    results = detector.detect_fire(test_image)
    assert isinstance(results, list)

---

📚 API Documentation

Endpoints

Method	Endpoint	Description
GET	/	Health check
POST	/api/detect	Detect fire in single image
POST	/api/detect-video	Process video file
POST	/api/detect-stream	Process live stream
GET	/api/alerts	Get alert history
GET	/api/stats	Get detection statistics

Example Request

curl -X POST "http://localhost:8000/api/detect" \
  -H "accept: application/json" \
  -F "file=@test_image.jpg" \
  -F "confidence_threshold=0.6"

Example Response

{
  "success": true,
  "detections": [
    {
      "class": "fire",
      "confidence": 0.87,
      "bbox": [120, 45, 340, 280],
      "area": 51700,
      "timestamp": "2025-02-17T10:30:00Z"
    }
  ],
  "image_size": [1920, 1080],
  "processing_time_ms": 45,
  "model_version": "YOLOv8n",
  "alert_sent": true
}

---

🎯 Use Cases

• 🌲 Forest Fire Monitoring - Integrate with surveillance cameras
• 🏭 Industrial Safety - Monitor factories and warehouses
• 🏙️ Smart Cities - City-wide fire detection network
• 🚁 Drone Surveillance - Aerial fire detection
• 🏢 Building Safety - Commercial building monitoring
• 🔬 Research - Computer vision and AI research

---

🔧 Troubleshooting

Low detection accuracy

• Adjust CONFIDENCE_THRESHOLD in .env (try 0.3-0.7)
• Ensure good lighting in input images
• Retrain model with more diverse dataset
• Check if fire type is represented in training data

Slow inference speed

• Use GPU instead of CPU (10x faster)
• Reduce image resolution
• Use YOLOv8n (nano) instead of larger models
• Enable batch processing for multiple images

False positives (detecting non-fire objects)

• Increase CONFIDENCE_THRESHOLD
• Add more negative examples to training data
• Implement temporal filtering for video streams
• Fine-tune model on your specific environment

API not accessible

• Check if port 8000 is available: netstat -an | grep 8000
• Verify firewall settings
• Try different host: --host 127.0.0.1 or --host 0.0.0.0
• Check logs for error messages

---

🤝 Contributing

Contributions are welcome! Please follow these guidelines:

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

Development Guidelines

• Follow PEP 8 style guide for Python
• Add docstrings to all functions
• Include unit tests for new features
• Update documentation as needed
• Keep commits atomic and well-described

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📋 Roadmap

• Mobile app (iOS/Android)
• Smoke detection in addition to fire
• Thermal imaging camera support
• Multi-language alert support
• Integration with emergency services APIs
• Real-time dashboard with maps
• Edge deployment (Raspberry Pi, NVIDIA Jetson)
• Cloud-based model serving
• Drone integration APIs
• Multi-camera synchronization

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📄 License

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

MIT License

Copyright (c) 2025 Salim Tagemouati

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction...

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👨‍💻 Author

Salim Tagemouati

• 🌐 GitHub: @SalimTag
• 💼 LinkedIn: Salim Tagemouati
• 📧 Email: salim.tagemouati@example.com
• 🌍 Location: Morocco
• 💡 Open to opportunities in AI/ML, Computer Vision, and Software Engineering

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🙏 Acknowledgments

• Ultralytics YOLOv8 - Outstanding object detection framework
• FastAPI - Modern, fast web framework
• OpenCV - Computer vision library
• Fire Dataset Contributors - For providing training data
• Open Source Community - For tools and inspiration

---

📖 Citation

If you use this project in your research or work, please cite:

@software{firedetection2025,
  author = {Tagemouati, Salim},
  title = {Fire Detection System: AI-Powered Wildfire Detection using YOLOv8},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/SalimTag/firedetection}
}

---

📊 Project Status

Status: 🟢 Active Development

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🔗 Related Projects

• Fire-Detection - Dataset preparation and augmentation tools
• Fire-Detection-backend - Backend API implementation

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⭐ Star this repository if you find it useful!

Made with ❤️ by Salim Tagemouati

_{🔥 Helping protect forests and lives through AI 🔥}