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Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications.
Sensors ( IF 3.9 ) Pub Date : 2020-07-15 , DOI: 10.3390/s20143923
Sonain Jamil 1 , Fawad 1 , MuhibUr Rahman 2 , Amin Ullah 3 , Salman Badnava 4 , Masoud Forsat 5 , Seyed Sajad Mirjavadi 5
Affiliation  

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.

中文翻译:

使用集成的视听功能对公共安全应用程序进行恶意UAV检测。

无人机(UAV)在监视,安全和远程监视中已变得很流行。但是,它们也对公共隐私构成了严重的安全威胁。对于安全配置公司来说,及时检测到恶意无人机是当前的一个开放研究问题。最近,该问题已通过多种方案解决。但是,每个计划都有局限性,例如极端天气条件和庞大的数据集需求。在本文中,我们提出了一个由手工和深度混合功能组成的新颖框架,可以从声音和图像信息中检测和定位恶意无人机。各自的数据集包括声音,鸟,飞机和雷暴的遮挡图像,以及分辨率和照度的变化。支持向量机(SVM)的各种内核被应用于对特征进行分类。与其他相关方法相比,实验结果验证了所提方案的改进性能。
更新日期:2020-07-15
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