Recurrent Motion Neural Network for Low Resolution Drone Detection

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dc.contributor.authorPratt, H.
dc.contributor.authorEvans, B.
dc.contributor.authorRowntree, T.
dc.contributor.authorReid, I.
dc.contributor.authorWiederman, S.
dc.contributor.conferenceDigital Image Computing: Techniques and Applications (DICTA) (29 Nov 2020 - 2 Dec 2020 : virtual online)
dc.date.issued2020
dc.description.abstractDrones are becoming increasingly prevalent in everyday usage with many commercial applications in fields such as construction work and agricultural surveying. Despite their common commercial use, drones have been recently used with malicious intent, such as airline disruptions at Gatwick Airport. With the emerging issue of safety concerns for the public and other airspace users, detecting and monitoring active drones in an area is crucial. This paper introduces a recurrent convolutional neural network (CNN) specifically designed for drone detection. This CNN can detect drones from down-sampled images by exploiting the temporal information of drones in flight and outperforms a state-of-the-art conventional object detector. Due to the lightweight and low resolution nature of this network, it can be mounted on a small processor and run at near real-time speeds.
dc.description.statementofresponsibilityHamish Pratt, Bernard Evans, Thomas Rowntree, Ian Reid and Steven Wiederman
dc.identifier.citationProceedings of the Digital Image Computing: Techniques and Applications (DICTA 2020), 2020, pp.1-7
dc.identifier.doi10.1109/DICTA51227.2020.9363377
dc.identifier.isbn9781728191089
dc.identifier.orcidPratt, H. [0000-0002-5350-4814]
dc.identifier.orcidEvans, B. [0000-0002-3517-3775]
dc.identifier.orcidReid, I. [0000-0001-7790-6423]
dc.identifier.orcidWiederman, S. [0000-0002-0902-803X]
dc.identifier.urihttps://hdl.handle.net/2440/137745
dc.language.isoen
dc.publisherIEEE
dc.publisher.placeonline
dc.relation.granthttp://purl.org/au-research/grants/arc/FT180100466
dc.relation.granthttp://purl.org/au-research/grants/arc/FL130100102
dc.relation.granthttp://purl.org/au-research/grants/arc/CE140100016
dc.relation.ispartofserieshttps://ieeexplore.ieee.org/xpl/conhome/9363348/proceeding
dc.rights©2020 IEEE
dc.source.urihttps://doi.org/10.1109/DICTA51227.2020
dc.subjectUAV; Object Detection; Motion
dc.titleRecurrent Motion Neural Network for Low Resolution Drone Detection
dc.typeConference paper
pubs.publication-statusPublished

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