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Malicious UAV detection using integrated audio and visual features for public safety applications

Sonain Jamil, Fawad, MuhibUr Rahman, Amin Ullah, Salman Badnava, Masoud Forsat and Seyed Sajad Mirjavadi

Article (2020)

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Unmanned 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.

Uncontrolled Keywords

AlexNet; feature extraction; localization; public safety; malicious drones; surveillance

Subjects: 2500 Electrical and electronic engineering > 2500 Electrical and electronic engineering
Department: Department of Electrical Engineering
Funders: Qatar National Library, Fidar Project Qaem Company (FPQ)
PolyPublie URL: https://publications.polymtl.ca/9426/
Journal Title: Sensors (vol. 20, no. 14)
Publisher: MDPI
DOI: 10.3390/s20143923
Official URL: https://doi.org/10.3390/s20143923
Date Deposited: 10 Aug 2023 10:07
Last Modified: 11 Aug 2023 05:47
Cite in APA 7: Jamil, S., Fawad, Rahman, M.U., Ullah, A., Badnava, S., Forsat, M., & Mirjavadi, S. S. (2020). Malicious UAV detection using integrated audio and visual features for public safety applications. Sensors, 20(14), 3923 (16 pages). https://doi.org/10.3390/s20143923


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