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A dataset for multi-sensor drone detection.

Fredrik Svanström1, Fernando Alonso-Fernandez2, Cristofer Englund2,3

  • 1Air Defence Regiment, Swedish Armed Forces, Sweden.

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Summary
This summary is machine-generated.

This study introduces a new, annotated multi-sensor database for detecting unmanned aerial vehicles (UAVs), also known as drones. The dataset includes diverse drone types, other flying objects, and varying distances for improved drone detection research.

Keywords:
Anti-drone systemsDrone detectionUAV detection

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Area of Science:

  • Computer Vision
  • Robotics
  • Aerospace Engineering

Background:

  • The proliferation of unmanned aerial vehicles (UAVs) necessitates robust detection methods.
  • Existing drone detection studies often lack detailed specifications of sensors, datasets, and detection ranges.
  • There is a gap in research utilizing thermal infrared cameras and analyzing detection performance as a function of distance.

Purpose of the Study:

  • To address the limitations in current drone detection research by providing a comprehensive, annotated multi-sensor database.
  • To facilitate fundamental studies on drone detection using a standardized public benchmark.
  • To enable research into sensor fusion for enhanced detection capabilities.

Main Methods:

  • Development of an annotated multi-sensor database including infrared and visible videos and audio files.
  • Inclusion of three distinct drone models (small, medium, performance-grade) and other flying objects (birds, airplanes, helicopters).
  • Dataset categorized into Close, Medium, and Distant ranges based on Detect, Recognize, Identify (DRI) requirements, with maximum sensor-to-target distance of 200m.

Main Results:

  • The database comprises 650 infrared and visible videos (203,328 annotated frames) and 90 audio files.
  • It features a higher number of classes compared to previous studies.
  • Data acquired at three Swedish airports using a pan-tilt platform-mounted sensor system.

Conclusions:

  • The created database serves as a valuable public benchmark for drone detection research.
  • It supports studies on detection performance across different distances and sensor modalities.
  • The dataset aids in advancing the development of more effective UAV detection systems.