Convolutional Neural Networks for Drone Model Classification

H. Dale; M. Antoniou; C. J. Baker; M. Jahangir; A. Catherall

doi:10.23919/EuRAD50154.2022.9784563

Convolutional Neural Networks for Drone Model Classification

H. Dale, M. Antoniou, C. J. Baker, M. Jahangir, A. Catherall

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This work establishes performances for drone group (fixed wing vs rotary wing), drone subgroup (fixed wing vs hexacopter vs quadcopter) and drone model classification using a convolutional neural network (CNN). Data is from an experimental campaign with nine different drone models flying along various trajectories. It is demonstrated that CNNs are very capable of drone recognition, with baseline performances as high as 98%.

Original language	English
Title of host publication	2021 18th European Radar Conference (EuRAD)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	361-364
Number of pages	4
ISBN (Electronic)	9782874870651
ISBN (Print)	9781665447232 (PoD)
DOIs	https://doi.org/10.23919/EuRAD50154.2022.9784563
Publication status	Published - 2 Jun 2021
Event	18th European Radar Conference, EuRAD 2021 - London, United Kingdom Duration: 5 Apr 2022 → 7 Apr 2022

Publication series

Name	European Radar Conference (EURAD)

Conference

Conference	18th European Radar Conference, EuRAD 2021
Country/Territory	United Kingdom
City	London
Period	5/04/22 → 7/04/22

Bibliographical note

Funding Information:
ACKNOWLEDGEMENTS This work was funded by the UK Engineering and Physical Sciences Research Council.

Publisher Copyright:
© 2022 European Microwave Association (EuMA).

Keywords

convolutional neural networks
deep learning
radar applications
staring radar
UAVs

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Signal Processing
Instrumentation

Access to Document

10.23919/EuRAD50154.2022.9784563

Cite this

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title = "Convolutional Neural Networks for Drone Model Classification",

abstract = "This work establishes performances for drone group (fixed wing vs rotary wing), drone subgroup (fixed wing vs hexacopter vs quadcopter) and drone model classification using a convolutional neural network (CNN). Data is from an experimental campaign with nine different drone models flying along various trajectories. It is demonstrated that CNNs are very capable of drone recognition, with baseline performances as high as 98%.",

keywords = "convolutional neural networks, deep learning, radar applications, staring radar, UAVs",

author = "H. Dale and M. Antoniou and Baker, {C. J.} and M. Jahangir and A. Catherall",

note = "Funding Information: ACKNOWLEDGEMENTS This work was funded by the UK Engineering and Physical Sciences Research Council. Publisher Copyright: {\textcopyright} 2022 European Microwave Association (EuMA).; 18th European Radar Conference, EuRAD 2021 ; Conference date: 05-04-2022 Through 07-04-2022",

year = "2021",

month = jun,

day = "2",

doi = "10.23919/EuRAD50154.2022.9784563",

language = "English",

isbn = "9781665447232 (PoD)",

series = "European Radar Conference (EURAD)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "361--364",

booktitle = "2021 18th European Radar Conference (EuRAD)",

}

Dale, H, Antoniou, M, Baker, CJ, Jahangir, M & Catherall, A 2021, Convolutional Neural Networks for Drone Model Classification. in 2021 18th European Radar Conference (EuRAD). European Radar Conference (EURAD), Institute of Electrical and Electronics Engineers (IEEE), pp. 361-364, 18th European Radar Conference, EuRAD 2021, London, United Kingdom, 5/04/22. https://doi.org/10.23919/EuRAD50154.2022.9784563

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T1 - Convolutional Neural Networks for Drone Model Classification

AU - Dale, H.

AU - Antoniou, M.

AU - Baker, C. J.

AU - Jahangir, M.

AU - Catherall, A.

PY - 2021/6/2

Y1 - 2021/6/2

N2 - This work establishes performances for drone group (fixed wing vs rotary wing), drone subgroup (fixed wing vs hexacopter vs quadcopter) and drone model classification using a convolutional neural network (CNN). Data is from an experimental campaign with nine different drone models flying along various trajectories. It is demonstrated that CNNs are very capable of drone recognition, with baseline performances as high as 98%.

AB - This work establishes performances for drone group (fixed wing vs rotary wing), drone subgroup (fixed wing vs hexacopter vs quadcopter) and drone model classification using a convolutional neural network (CNN). Data is from an experimental campaign with nine different drone models flying along various trajectories. It is demonstrated that CNNs are very capable of drone recognition, with baseline performances as high as 98%.

KW - convolutional neural networks

KW - deep learning

KW - radar applications

KW - staring radar

KW - UAVs

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U2 - 10.23919/EuRAD50154.2022.9784563

DO - 10.23919/EuRAD50154.2022.9784563

M3 - Conference contribution

AN - SCOPUS:85133139109

SN - 9781665447232 (PoD)

T3 - European Radar Conference (EURAD)

SP - 361

EP - 364

BT - 2021 18th European Radar Conference (EuRAD)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 18th European Radar Conference, EuRAD 2021

Y2 - 5 April 2022 through 7 April 2022

ER -

Convolutional Neural Networks for Drone Model Classification

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this