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المؤلفJamil, Sonain
المؤلفFawad
المؤلفRahman, MuhibUr
المؤلفUllah, Amin
المؤلفBadnava, Salman
المؤلفForsat, Masoud
المؤلفMirjavadi, Seyed S.
تاريخ الإتاحة2023-02-12T06:20:51Z
تاريخ النشر2020
اسم المنشورSensors (Switzerland)
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.3390/s20143923
معرّف المصادر الموحدhttp://hdl.handle.net/10576/39945
الملخصUnmanned 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.
راعي المشروعThe 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).
اللغةen
الناشرMDPI AG
الموضوعAlexNet
Feature extraction
Localization
Malicious drones
Public safety
Surveillance
العنوانMalicious uav detection using integrated audio and visual features for public safety applications
النوعArticle
الصفحات16-Jan
رقم العدد14
رقم المجلد20
dc.accessType Abstract Only


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