Distinguishing Malicious Drones Using Vision Transformer

نویسندگان

چکیده

Drones are commonly used in numerous applications, such as surveillance, navigation, spraying pesticides autonomous agricultural systems, various military services, etc., due to their variable sizes and workloads. However, malicious drones that carry harmful objects often adversely intrude restricted areas attack critical public places. Thus, the timely detection of can prevent potential harm. This article proposes a vision transformer (ViT) based framework distinguish between drones. In proposed ViT model, drone images split into fixed-size patches; then, linearly embeddings position applied, resulting sequence vectors is finally fed standard encoder. During classification, an additional learnable classification token associated used. The compared with several handcrafted deep convolutional neural networks (D-CNN), which reveal model has achieved accuracy 98.3%, outperforming D-CNNs models. Additionally, superiority illustrated by comparing it existing state-of-the-art drone-detection methods.

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ژورنال

عنوان ژورنال: AI

سال: 2022

ISSN: ['2673-2688']

DOI: https://doi.org/10.3390/ai3020016