Sonain Jamil, Fawad, MuhibUr Rahman, Amin Ullah, Salman Badnava, Masoud Forsat et Seyed Sajad Mirjavadi
Article de revue (2020)
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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.
Mots clés
AlexNet; feature extraction; localization; public safety; malicious drones; surveillance
Sujet(s): | 2500 Génie électrique et électronique > 2500 Génie électrique et électronique |
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Département: | Département de génie électrique |
Organismes subventionnaires: | Qatar National Library, Fidar Project Qaem Company (FPQ) |
URL de PolyPublie: | https://publications.polymtl.ca/9426/ |
Titre de la revue: | Sensors (vol. 20, no 14) |
Maison d'édition: | MDPI |
DOI: | 10.3390/s20143923 |
URL officielle: | https://doi.org/10.3390/s20143923 |
Date du dépôt: | 10 août 2023 10:07 |
Dernière modification: | 05 avr. 2024 15:11 |
Citer en 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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