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Improving real-time drone detection for counter-drone systems

Published online by Cambridge University Press:  16 June 2021

E. Çetin Open the ORCID record for E. Çetin [Opens in a new window] ,
C. Barrado  and
E. Pastor
E. Çetin*
Affiliation:
Technical University of Catalonia UPC BarcelonaTech Computer Architecture Department BarcelonaSpain
C. Barrado
Affiliation:
Technical University of Catalonia UPC BarcelonaTech Computer Architecture Department BarcelonaSpain
E. Pastor
Affiliation:
Technical University of Catalonia UPC BarcelonaTech Computer Architecture Department BarcelonaSpain
*
ender.cetin@upc.edu
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Abstract

The number of unmanned aerial vehicles (UAVs, also known as drones) has increased dramatically in the airspace worldwide for tasks such as surveillance, reconnaissance, shipping and delivery. However, a small number of them, acting maliciously, can raise many security risks. Recent Artificial Intelligence (AI) capabilities for object detection can be very useful for the identification and classification of drones flying in the airspace and, in particular, are a good solution against malicious drones. A number of counter-drone solutions are being developed, but the cost of drone detection ground systems can also be very high, depending on the number of sensors deployed and powerful fusion algorithms. We propose a low-cost counter-drone solution composed uniquely by a guard-drone that should be able to detect, locate and eliminate any malicious drone. In this paper, a state-of-the-art object detection algorithm is used to train the system to detect drones. Three existing object detection models are improved by transfer learning and tested for real-time drone detection. Training is done with a new dataset of drone images, constructed automatically from a very realistic flight simulator. While flying, the guard-drone captures random images of the area, while at the same time, a malicious drone is flying too. The drone images are auto-labelled using the location and attitude information available in the simulator for both drones. The world coordinates for the malicious drone position must then be projected into image pixel coordinates. The training and test results show a minimum accuracy improvement of 22% with respect to state-of-the-art object detection models, representing promising results that enable a step towards the construction of a fully autonomous counter-drone system.

Keywords

Counter-DroneUAVDronesObject DetectionYOLOEfficientNetdeep learningAirsim
Type
Research Article
Information
The Aeronautical Journal , Volume 125 , Issue 1292 , October 2021 , pp. 1871 - 1896
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Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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