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Distinguishing Malicious Drones Using Vision Transformer

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Department of Electronics Engineering, Sejong University, Seoul 05006, Korea
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School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
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Aerospace Engineering Department, University of Michigan, Ann Arbor, MI 48109, USA
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Authors to whom correspondence should be addressed.
Academic Editor: Rafał Dreżewski
AI 2022, 3(2), 260-273; https://doi.org/10.3390/ai3020016
Received: 6 March 2022 / Revised: 25 March 2022 / Accepted: 29 March 2022 / Published: 31 March 2022
(This article belongs to the Special Issue Emerging Trends of Deep Learning in AI: Challenges and Methodologies)
Drones are commonly used in numerous applications, such as surveillance, navigation, spraying pesticides in autonomous agricultural systems, various military services, etc., due to their variable sizes and workloads. However, malicious drones that carry harmful objects are often adversely used to intrude restricted areas and attack critical public places. Thus, the timely detection of malicious drones can prevent potential harm. This article proposes a vision transformer (ViT) based framework to distinguish between drones and malicious drones. In the proposed ViT based model, drone images are split into fixed-size patches; then, linearly embeddings and position embeddings are applied, and the resulting sequence of vectors is finally fed to a standard ViT encoder. During classification, an additional learnable classification token associated to the sequence is used. The proposed framework is compared with several handcrafted and deep convolutional neural networks (D-CNN), which reveal that the proposed model has achieved an accuracy of 98.3%, outperforming various handcrafted and D-CNNs models. Additionally, the superiority of the proposed model is illustrated by comparing it with the existing state-of-the-art drone-detection methods. View Full-Text
Keywords: vision transformer; deep convolutional neural networks; deep learning; malicious drones; classification; drones vision transformer; deep convolutional neural networks; deep learning; malicious drones; classification; drones
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MDPI and ACS Style

Jamil, S.; Abbas, M.S.; Roy, A.M. Distinguishing Malicious Drones Using Vision Transformer. AI 2022, 3, 260-273. https://doi.org/10.3390/ai3020016

AMA Style

Jamil S, Abbas MS, Roy AM. Distinguishing Malicious Drones Using Vision Transformer. AI. 2022; 3(2):260-273. https://doi.org/10.3390/ai3020016

Chicago/Turabian Style

Jamil, Sonain, Muhammad Sohail Abbas, and Arunabha M. Roy. 2022. "Distinguishing Malicious Drones Using Vision Transformer" AI 3, no. 2: 260-273. https://doi.org/10.3390/ai3020016

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