Sonain Jamil, Fawad, MuhibUr Rahman, Amin Ullah, Salman Badnava, Masoud Forsat and Seyed Sajad Mirjavadi
Article (2020)
|
Open Access to the full text of this document Published Version Terms of Use: Creative Commons Attribution Download (5MB) |
Résumé
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
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: | 26 Sep 2024 03:21 |
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 |
---|---|
Statistics
Total downloads
Downloads per month in the last year
Origin of downloads
Dimensions