A dataset for multi-sensor drone detection

The use of small and remotely controlled unmanned aerial vehicles (UAVs), referred to as drones, has increased dramatically in recent years, both for professional and recreative purposes. This goes in parallel with (intentional or unintentional) misuse episodes, with an evident threat to the safety...

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Autores principales:	Fredrik Svanström, Fernando Alonso-Fernandez, Cristofer Englund
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Drone detection UAV detection Anti-drone systems Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/3a14dcf959694712b42cb9c86bc0940a
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id	oai:doaj.org-article:3a14dcf959694712b42cb9c86bc0940a
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spelling	oai:doaj.org-article:3a14dcf959694712b42cb9c86bc0940a2021-11-04T04:32:32ZA dataset for multi-sensor drone detection2352-340910.1016/j.dib.2021.107521https://doaj.org/article/3a14dcf959694712b42cb9c86bc0940a2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352340921007976https://doaj.org/toc/2352-3409The use of small and remotely controlled unmanned aerial vehicles (UAVs), referred to as drones, has increased dramatically in recent years, both for professional and recreative purposes. This goes in parallel with (intentional or unintentional) misuse episodes, with an evident threat to the safety of people or facilities [1]. As a result, the detection of UAV has also emerged as a research topic [2].Most of the existing studies on drone detection fail to specify the type of acquisition device, the drone type, the detection range, or the employed dataset. The lack of proper UAV detection studies employing thermal infrared cameras is also acknowledged as an issue, despite its success in detecting other types of targets [2]. Beside, we have not found any previous study that addresses the detection task as a function of distance to the target. Sensor fusion is indicated as an open research issue as well to achieve better detection results in comparison to a single sensor, although research in this direction is scarce too [3–6].To help in counteracting the mentioned issues and allow fundamental studies with a common public benchmark, we contribute with an annotated multi-sensor database for drone detection that includes infrared and visible videos and audio files. The database includes three different drones, a small-sized model (Hubsan H107D+), a medium-sized drone (DJI Flame Wheel in quadcopter configuration), and a performance-grade model (DJI Phantom 4 Pro). It also includes other flying objects that can be mistakenly detected as drones, such as birds, airplanes or helicopters. In addition to using several different sensors, the number of classes is higher than in previous studies [4]. The video part contains 650 infrared and visible videos (365 IR and 285 visible) of drones, birds, airplanes and helicopters. Each clip is of ten seconds, resulting in a total of 203,328 annotated frames. The database is complemented with 90 audio files of the classes drones, helicopters and background noise.To allow studies as a function of the sensor-to-target distance, the dataset is divided into three categories (Close, Medium, Distant) according to the industry-standard Detect, Recognize and Identify (DRI) requirements [7], built on the Johnson criteria [8]. Given that the drones must be flown within visual range due to regulations, the largest sensor-to-target distance for a drone in the dataset is 200 m, and acquisitions are made in daylight. The data has been obtained at three airports in Sweden: Halmstad Airport (IATA code: HAD/ICAO code: ESMT), Gothenburg City Airport (GSE/ESGP) and Malmö Airport (MMX/ESMS). The acquisition sensors are mounted on a pan-tilt platform that steers the cameras to the objects of interest. All sensors and the platform are controlled with a standard laptop vis a USB hub.Fredrik SvanströmFernando Alonso-FernandezCristofer EnglundElsevierarticleDrone detectionUAV detectionAnti-drone systemsComputer applications to medicine. Medical informaticsR858-859.7Science (General)Q1-390ENData in Brief, Vol 39, Iss , Pp 107521- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Drone detection UAV detection Anti-drone systems Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
spellingShingle	Drone detection UAV detection Anti-drone systems Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390 Fredrik Svanström Fernando Alonso-Fernandez Cristofer Englund A dataset for multi-sensor drone detection
description	The use of small and remotely controlled unmanned aerial vehicles (UAVs), referred to as drones, has increased dramatically in recent years, both for professional and recreative purposes. This goes in parallel with (intentional or unintentional) misuse episodes, with an evident threat to the safety of people or facilities [1]. As a result, the detection of UAV has also emerged as a research topic [2].Most of the existing studies on drone detection fail to specify the type of acquisition device, the drone type, the detection range, or the employed dataset. The lack of proper UAV detection studies employing thermal infrared cameras is also acknowledged as an issue, despite its success in detecting other types of targets [2]. Beside, we have not found any previous study that addresses the detection task as a function of distance to the target. Sensor fusion is indicated as an open research issue as well to achieve better detection results in comparison to a single sensor, although research in this direction is scarce too [3–6].To help in counteracting the mentioned issues and allow fundamental studies with a common public benchmark, we contribute with an annotated multi-sensor database for drone detection that includes infrared and visible videos and audio files. The database includes three different drones, a small-sized model (Hubsan H107D+), a medium-sized drone (DJI Flame Wheel in quadcopter configuration), and a performance-grade model (DJI Phantom 4 Pro). It also includes other flying objects that can be mistakenly detected as drones, such as birds, airplanes or helicopters. In addition to using several different sensors, the number of classes is higher than in previous studies [4]. The video part contains 650 infrared and visible videos (365 IR and 285 visible) of drones, birds, airplanes and helicopters. Each clip is of ten seconds, resulting in a total of 203,328 annotated frames. The database is complemented with 90 audio files of the classes drones, helicopters and background noise.To allow studies as a function of the sensor-to-target distance, the dataset is divided into three categories (Close, Medium, Distant) according to the industry-standard Detect, Recognize and Identify (DRI) requirements [7], built on the Johnson criteria [8]. Given that the drones must be flown within visual range due to regulations, the largest sensor-to-target distance for a drone in the dataset is 200 m, and acquisitions are made in daylight. The data has been obtained at three airports in Sweden: Halmstad Airport (IATA code: HAD/ICAO code: ESMT), Gothenburg City Airport (GSE/ESGP) and Malmö Airport (MMX/ESMS). The acquisition sensors are mounted on a pan-tilt platform that steers the cameras to the objects of interest. All sensors and the platform are controlled with a standard laptop vis a USB hub.
format	article
author	Fredrik Svanström Fernando Alonso-Fernandez Cristofer Englund
author_facet	Fredrik Svanström Fernando Alonso-Fernandez Cristofer Englund
author_sort	Fredrik Svanström
title	A dataset for multi-sensor drone detection
title_short	A dataset for multi-sensor drone detection
title_full	A dataset for multi-sensor drone detection
title_fullStr	A dataset for multi-sensor drone detection
title_full_unstemmed	A dataset for multi-sensor drone detection
title_sort	dataset for multi-sensor drone detection
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/3a14dcf959694712b42cb9c86bc0940a
work_keys_str_mv	AT fredriksvanstrom adatasetformultisensordronedetection AT fernandoalonsofernandez adatasetformultisensordronedetection AT cristoferenglund adatasetformultisensordronedetection AT fredriksvanstrom datasetformultisensordronedetection AT fernandoalonsofernandez datasetformultisensordronedetection AT cristoferenglund datasetformultisensordronedetection
_version_	1718445268026785792

A dataset for multi-sensor drone detection

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