DomeWatch – Hybrid Anti-Drone Security System

DomeWatch is a hybrid anti-drone security system designed to detect and track unauthorized drones using AI-powered computer vision. It is aimed at enhancing airspace security for restricted areas such as borders, airports, military zones, and public events. This prototype was developed as part of a hackathon project and lays the foundation for future integration with IoT-based countermeasures.

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Features

• Real-time drone detection using computer vision techniques
• Deep learning-based object detection model (YOLOv8)
• Surveillance through live video stream analysis
• Logging of drone detections with timestamps
• Modular structure for easy integration with additional systems (e.g., jammers, alert systems)

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Project Structure

DomeWatch/ │ ├── dataset/ # Sample dataset for training or testing ├── models/ # YOLOv8 weights ├── src/ # Source code │ ├── detector.py # Drone detection logic │ ├── camera_stream.py # Video input stream setup │ └── utils.py # Utility functions ├── results/ # Output screenshots or logs ├── requirements.txt # List of dependencies └── README.md # Project documentation

yaml Copy Edit

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How It Works

1. Captures video input from a webcam or external camera.
2. Processes each frame using a YOLOv8-based object detection model.
3. Identifies drones and logs the detection time and data.
4. Can be extended to trigger alerts or activate defense mechanisms.

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Getting Started

Prerequisites

• Python 3.8 or higher
• pip
• OpenCV
• PyTorch
• Ultralytics (YOLOv8)

Installation

1. Clone this repository:

   git clone https://github.com/Sneha73685/Maha-Hackathon.git
   cd DomeWatch

Install the required dependencies:

bash Copy Edit pip install -r requirements.txt Download the YOLOv8 weights and place them in the models/ folder.

Run the detection script:

bash Copy Edit python src/detector.py Use Cases Securing national borders

Airport and airspace surveillance

Protection of military zones

Safety at public events and private properties

Future Enhancements Integration with radar and RF-based tracking

Automatic counter-drone actions (e.g., jammers, signal disruptors)

Real-time dashboard for monitoring

Alerts via SMS, email, or siren systems

Drone classification and threat level assessment

Team & Acknowledgements This project was developed by Sneha and team during a national-level hackathon. Special thanks to the open-source community, especially contributors of the YOLOv8 framework by Ultralytics.

License This project is licensed under the MIT License.