Simuletic/Airport_Drone_Threat_And_Safety_Dataset

✈️ Simuletic Airport Drone Threat & Safety Dataset

Synthetic Benchmark for Aviation Security & Rogue Drone Detection

Overview

This is an open-source synthetic dataset designed to solve a critical issue in Aviation Security: Runway Incursions by Rogue Drones.

Detecting a small drone against the complex background of a busy airport (moving planes, flashing lights, tarmac texture) is a massive challenge for standard AI. Real-world training data is nearly impossible to capture due to strict flight regulations.

This dataset provides high-fidelity synthetic imagery of drones in terminal and runway environments, specifically engineered to reduce false positives and improve detection rates in critical infrastructure.

🚀 Need more data?

This is a sample dataset by Simuletic. We provide hyper-realistic synthetic data to solve "edge cases" in Security AI—from Abandoned Luggage to Perimeter Breaches.

👉 Get full-scale datasets & video sequences: simuletic.com/datasets

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✨ Key Features

• ✈️ Complex Backgrounds: Drones are captured against realistic airport clutter—terminals, hangars, and tarmac—to train models to distinguish threats from infrastructure.
• ☁️ Adverse Weather: Includes difficult conditions like Fog, Rain, and Night operations, where standard optical sensors often fail.
• 🔒 Privacy-First: 100% Synthetic. Completely GDPR compliant with no real faces, license plates, or sensitive security protocols filmed.
• 🎯 Small Object Focus: Targets usually occupy <5% of the frame, simulating long-range detection required for perimeter defense.

📂 Dataset Structure

The dataset follows the standard YOLOv8 / YOLO11 format.

• images/: High-fidelity synthetic .jpg files.
• labels/: .txt files containing class ID and normalized bounding boxes.

ID	Class Name	Description
0	drone	Consumer quadcopters and rogue UAVs

⚙️ YAML Configuration

To train immediately with YOLO, copy this into your data.yaml:

# Simuletic Airport Drone Configuration
path: /path/to/dataset
train: images
val: images

nc: 1
names: ['drone']

## Use Cases

Runway Safety: Detect rogue drones entering flight paths or taxiways.

Bird vs. Drone Classification: Train models to differentiate between mechanical drones and biological targets (birds) to reduce false alarms.

Perimeter Defense: Monitoring fence lines and hangar approaches for unauthorized aerial surveillance.

## Ethics & License

Synthetic Nature: This data is 100% computer-generated by Simuletic. It is free from GDPR concerns.

 License: CC BY 4.0. You are free to use and adapt this data for research or commercial proofs-of-concept, provided you give appropriate credit to Simuletic.

## Citation

If you use this dataset in your research, please cite:

@dataset{simuletic_airport_drone_2025,
  author = {Simuletic Team},
  title = {Simuletic Airport Drone Threat & Safety Dataset},
  year = {2025},
  url = {[https://simuletic.com](https://simuletic.com)}
}

Feedback? Reach out via simuletic.com or the "Issues" tab here on Kaggle.