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Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications

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submitted on 2024-11-18, 07:43 and posted on 2024-11-18, 07:44 authored by Sonain Jamil, Fawad, MuhibUr Rahman, Amin Ullah, Salman Badnava, Masoud Forsat, Seyed Sajad Mirjavadi

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.

Other Information

Published in: Sensors
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.3390/s20143923

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

MDPI

Publication Year

  • 2020

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Qatar University
  • College of Engineering - QU

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