Drone Detection and Tracking in Real-Time by Fusion of Different Sensing Modalities

Automatic detection of flying drones is a key issue where its presence, especially if unauthorized, can create risky situations or compromise security. Here, we design and evaluate a multi-sensor drone detection system. In conjunction with standard video cameras and microphone sensors, we explore th...

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Veröffentlicht in:Drones (Basel) Jg. 6; H. 11; S. 317
Hauptverfasser: Svanström, Fredrik, Alonso-Fernandez, Fernando, Englund, Cristofer
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 2022
Schlagworte:
Aircraft
Airports
anti-drone systems
Cameras
Classification
Datasets
Drone aircraft
drone detection
Drones
Infrared cameras
Machine learning
Methods
Object recognition
Radar systems
Sensors
Software
Tracking systems
UAV detection
Unmanned aerial vehicles
Sweden
ISSN:2504-446X, 2504-446X
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Zusammenfassung:Automatic detection of flying drones is a key issue where its presence, especially if unauthorized, can create risky situations or compromise security. Here, we design and evaluate a multi-sensor drone detection system. In conjunction with standard video cameras and microphone sensors, we explore the use of thermal infrared cameras, pointed out as a feasible and promising solution that is scarcely addressed in the related literature. Our solution integrates a fish-eye camera as well to monitor a wider part of the sky and steer the other cameras towards objects of interest. The sensing solutions are complemented with an ADS-B receiver, a GPS receiver, and a radar module. However, our final deployment has not included the latter due to its limited detection range. The thermal camera is shown to be a feasible solution as good as the video camera, even if the camera employed here has a lower resolution. Two other novelties of our work are the creation of a new public dataset of multi-sensor annotated data that expands the number of classes compared to existing ones, as well as the study of the detector performance as a function of the sensor-to-target distance. Sensor fusion is also explored, showing that the system can be made more robust in this way, mitigating false detections of the individual sensors.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2504-446X
2504-446X
DOI:10.3390/drones6110317