Avalanche Victim Detector

Abstract

Avalanches are perilous events requiring immediate action to save lives. Existing detection methods often fall short in harsh conditions, making AI/ML-powered drone technology a necessity. This project leverages the DJI Matrice 300 RTK drone equipped with a Thermal Camera, Beacon Transceiver, and Metal Detector to detect and locate avalanche victims swiftly.

The drone captures thermal imagery to identify humans, aided by safety gear detection through the Beacon Transceiver and Metal Detector. A user-friendly dashboard logs victim locations with timestamps, classifications (e.g., human, animal, wood), and geographical landmarks, significantly reducing search times and mortality rates.

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Features

1. Thermal Imaging:

   • Identifies human heat signatures.
   • Differentiates between debris types: Human, Animal, or Wood.

2. Beacon and Metal Detection:

   • Locates safety equipment to expedite searches.

3. User-Friendly Dashboard:

   • Displays classification logs with voice notifications.
   • Highlights victim locations with Latitude and Longitude.

4. Drone Specifications:

   • DJI Matrice 300 RTK for resilient operation in extreme climates.

5. Efficient Workflow:

   • Rapid terrain surveillance.
   • Classification logs to aid rescue teams in the critical 60-minute window.

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Technology Stack

• Hardware: DJI Matrice 300 RTK, Thermal Camera, Beacon Transceiver, Metal Detector.
• Software: Python, OpenCV, AI/ML models for classification.
• Dashboard: Web-based interface for real-time monitoring and logging.

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Usage

• Setup: Mount the sensors and camera on the drone.
• Operation: Fly the drone over avalanche-prone areas.
• Monitoring: View classification logs and victim locations on a dashboard.

Results

The proposed system improves detection accuracy and reduces response time, ensuring better survival chances for avalanche victims.

Contributing

We welcome contributions from the community! If you'd like to contribute:

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

License

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