Drone Detection & Classification with Surveillance ‘Radar On-The-Move’ and YOLO

Hani Haifawi; Francesco Fioranelli; Alexander Yarovoy; Rob van der Meer

doi:10.1109/RadarConf2351548.2023.10149588

Drone Detection & Classification with Surveillance ‘Radar On-The-Move’ and YOLO

Hani Haifawi, Francesco Fioranelli, Alexander Yarovoy, Rob van der Meer

Microwave Sensing, Signals & Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

71 Downloads (Pure)

Abstract

A new method to jointly detect and classify drones using a moving surveillance radar system (‘radar on-the-move’) and computer vision is presented. While most conventional counter-drone radar-based techniques focus on time-frequency distributions to obtain classification features, such approaches are limited in volumetric spatial coverage. To compensate for this, surveillance radars that offer full spatial coverage are used, but the determination of the best detection and classification approach to be applied on the resulting data is still an open challenge. In this paper a framework is proposed that combines deep learning approaches from computer vision, specifically the You Only Look Once (YOLO) network, with data from the moving surveillance radar produced by Robin Radar Systems B.V. This framework allows to jointly detect and label targets based on range-Doppler images generated in real-time. The method is validated on experimental data, with preliminary results on a small dataset showing precision, recall, mean average precision (mAP@0.5) and Area Under Curve (AUC) of over 99%

Original language	English
Title of host publication	Proceedings of the 2023 IEEE Radar Conference (RadarConf23)
Place of Publication	Piscataway
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Electronic)	978-1-6654-3669-4
ISBN (Print)	978-1-6654-3670-0
DOIs	https://doi.org/10.1109/RadarConf2351548.2023.10149588
Publication status	Published - 2023
Event	2023 IEEE Radar Conference (RadarConf23) - San Antonio, United States Duration: 1 May 2023 → 5 May 2023

Conference

Conference	2023 IEEE Radar Conference (RadarConf23)
Country/Territory	United States
City	San Antonio
Period	1/05/23 → 5/05/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

drone detection
drone classification
surveillance radar
YOLO

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10.1109/RadarConf2351548.2023.10149588

Drone_Detection_amp_Classification_with_Surveillance_Radar_On-The-Move_and_YOLO (1)Final published version, 2.08 MB

Cite this

@inproceedings{53fccd0c363745b4bd64f5bc14648a98,

title = "Drone Detection & Classification with Surveillance {\textquoteleft}Radar On-The-Move{\textquoteright} and YOLO",

abstract = "A new method to jointly detect and classify drones using a moving surveillance radar system ({\textquoteleft}radar on-the-move{\textquoteright}) and computer vision is presented. While most conventional counter-drone radar-based techniques focus on time-frequency distributions to obtain classification features, such approaches are limited in volumetric spatial coverage. To compensate for this, surveillance radars that offer full spatial coverage are used, but the determination of the best detection and classification approach to be applied on the resulting data is still an open challenge. In this paper a framework is proposed that combines deep learning approaches from computer vision, specifically the You Only Look Once (YOLO) network, with data from the moving surveillance radar produced by Robin Radar Systems B.V. This framework allows to jointly detect and label targets based on range-Doppler images generated in real-time. The method is validated on experimental data, with preliminary results on a small dataset showing precision, recall, mean average precision (mAP@0.5) and Area Under Curve (AUC) of over 99%",

keywords = "drone detection, drone classification, surveillance radar, YOLO",

author = "Hani Haifawi and Francesco Fioranelli and Alexander Yarovoy and {van der Meer}, Rob",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 2023 IEEE Radar Conference (RadarConf23) ; Conference date: 01-05-2023 Through 05-05-2023",

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doi = "10.1109/RadarConf2351548.2023.10149588",

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Haifawi, H, Fioranelli, F , Yarovoy, A & van der Meer, R 2023, Drone Detection & Classification with Surveillance ‘Radar On-The-Move’ and YOLO. in Proceedings of the 2023 IEEE Radar Conference (RadarConf23). IEEE, Piscataway, pp. 1-6, 2023 IEEE Radar Conference (RadarConf23), San Antonio, United States, 1/05/23. https://doi.org/10.1109/RadarConf2351548.2023.10149588

Drone Detection & Classification with Surveillance ‘Radar On-The-Move’ and YOLO. / Haifawi, Hani; Fioranelli, Francesco ; Yarovoy, Alexander et al.
Proceedings of the 2023 IEEE Radar Conference (RadarConf23). Piscataway: IEEE, 2023. p. 1-6.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2023

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N2 - A new method to jointly detect and classify drones using a moving surveillance radar system (‘radar on-the-move’) and computer vision is presented. While most conventional counter-drone radar-based techniques focus on time-frequency distributions to obtain classification features, such approaches are limited in volumetric spatial coverage. To compensate for this, surveillance radars that offer full spatial coverage are used, but the determination of the best detection and classification approach to be applied on the resulting data is still an open challenge. In this paper a framework is proposed that combines deep learning approaches from computer vision, specifically the You Only Look Once (YOLO) network, with data from the moving surveillance radar produced by Robin Radar Systems B.V. This framework allows to jointly detect and label targets based on range-Doppler images generated in real-time. The method is validated on experimental data, with preliminary results on a small dataset showing precision, recall, mean average precision (mAP@0.5) and Area Under Curve (AUC) of over 99%

AB - A new method to jointly detect and classify drones using a moving surveillance radar system (‘radar on-the-move’) and computer vision is presented. While most conventional counter-drone radar-based techniques focus on time-frequency distributions to obtain classification features, such approaches are limited in volumetric spatial coverage. To compensate for this, surveillance radars that offer full spatial coverage are used, but the determination of the best detection and classification approach to be applied on the resulting data is still an open challenge. In this paper a framework is proposed that combines deep learning approaches from computer vision, specifically the You Only Look Once (YOLO) network, with data from the moving surveillance radar produced by Robin Radar Systems B.V. This framework allows to jointly detect and label targets based on range-Doppler images generated in real-time. The method is validated on experimental data, with preliminary results on a small dataset showing precision, recall, mean average precision (mAP@0.5) and Area Under Curve (AUC) of over 99%

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SP - 1

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BT - Proceedings of the 2023 IEEE Radar Conference (RadarConf23)

PB - IEEE

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T2 - 2023 IEEE Radar Conference (RadarConf23)

Y2 - 1 May 2023 through 5 May 2023

ER -

Drone Detection & Classification with Surveillance ‘Radar On-The-Move’ and YOLO

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Keywords

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