Fast Drone Detection With Optimized Feature Capture and Modeling Algorithms

Tu, Xiaohan; Zhang, Chuanhao; Zhuang, Haiyan; Liu, Siping; Li, Renfa

doi:10.1109/access.2024.3438991

IEEE Access

2024

DOI: 10.1109/access.2024.3438991

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Fast Drone Detection With Optimized Feature Capture and Modeling Algorithms

Xiaohan Tu,

Chuanhao Zhang,

Haiyan Zhuang

et al.

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A Survey on Vision-Based Anti Unmanned Aerial Vehicles Methods

Wang,

Li,

Mao

et al. 2024

Drones

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The rapid development and widespread application of Unmanned Aerial Vehicles (UAV) have raised significant concerns about safety and privacy, thus requiring powerful anti-UAV systems. This survey provides an overview of anti-UAV detection and tracking methods in recent years. Firstly, we emphasize the key challenges of existing anti-UAV and delve into various detection and tracking methods. It is noteworthy that our study emphasizes the shift toward deep learning to enhance detection accuracy and tracking performance. Secondly, the survey organizes some public datasets, provides effective links, and discusses the characteristics and limitations of each dataset. Next, by analyzing current research trends, we have identified key areas of innovation, including the progress of deep learning techniques in real-time detection and tracking, multi-sensor fusion systems, and the automatic switching mechanisms that adapt to different conditions. Finally, this survey discusses the limitations and future research directions. This paper aims to deepen the understanding of innovations in anti-UAV detection and tracking methods. Hopefully our work can offer a valuable resource for researchers and practitioners involved in anti-UAV research.

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A Survey on Vision-Based Anti Unmanned Aerial Vehicles Methods

Wang,

Li,

Mao

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

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Fast Drone Detection With Optimized Feature Capture and Modeling Algorithms

Cited by 1 publication

References 20 publications

A Survey on Vision-Based Anti Unmanned Aerial Vehicles Methods

A Survey on Vision-Based Anti Unmanned Aerial Vehicles Methods

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