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AuthorJamil, Sonain
AuthorFawad
AuthorRahman, MuhibUr
AuthorUllah, Amin
AuthorBadnava, Salman
AuthorForsat, Masoud
AuthorMirjavadi, Seyed S.
Available date2023-02-12T06:20:51Z
Publication Date2020
Publication NameSensors (Switzerland)
ResourceScopus
URIhttp://dx.doi.org/10.3390/s20143923
URIhttp://hdl.handle.net/10576/39945
AbstractUnmanned aerial vehicles (UAVs) have become popular in surveillance, security, and remote monitoring. However, they also pose serious security threats to public privacy. The timely detection of a malicious drone is currently an open research issue for security provisioning companies. Recently, the problem has been addressed by a plethora of schemes. However, each plan has a limitation, such as extreme weather conditions and huge dataset requirements. In this paper, we propose a novel framework consisting of the hybrid handcrafted and deep feature to detect and localize malicious drones from their sound and image information. The respective datasets include sounds and occluded images of birds, airplanes, and thunderstorms, with variations in resolution and illumination. Various kernels of the support vector machine (SVM) are applied to classify the features. Experimental results validate the improved performance of the proposed scheme compared to other related methods.
SponsorThe publication of this article was funded by the Qatar National Library. Seyed Sajad Mirjavadi also appreciates the help from the Fidar Project Qaem Company (FPQ).
Languageen
PublisherMDPI AG
SubjectAlexNet
Feature extraction
Localization
Malicious drones
Public safety
Surveillance
TitleMalicious uav detection using integrated audio and visual features for public safety applications
TypeArticle
Pagination16-Jan
Issue Number14
Volume Number20
dc.accessType Abstract Only


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