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

Sensors
Sonain JamilSeyed Sajad Mirjavadi

Abstract

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.

References

Apr 20, 2012·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Subrahmanyam MuralaR Balasubramanian

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Citations

Sep 3, 2020·Sensors·Cătălin DumitrescuAugustin Semenescu

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Methods Mentioned

BETA
feature extraction

Software Mentioned

LETRIST
Inceptionv3
GoogLeNet
AlexNet
VGG
MATLAB

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