Design of the "Sky Watcher" Intelligent Anti-Drone System

In recent years, the rapid development of drone technology and the reduction in manufacturing costs have led to a significant increase in the number of drones. Consequently, illegal intrusions or accidental entries by drones have resulted in infringements of personal privacy and threats to public safety. Therefore, it has become urgent to develop effective anti-drone systems. This paper proposes a computer vision-based drone defense detection system that not only detects drones but also distinguishes between drones and birds, addressing the limitations of traditional radar and infrared detection technologies in target identification. Additionally, this paper creates a "drone-bird" dataset and further enhances it through data augmentation techniques, improving the model's generalization and recognition capabilities for targets of different sizes and poses. Moreover, a dynamic detection head based on the attention mechanism is introduced into the model, significantly enhancing the detection efficiency and accuracy of small drones, thereby improving the performance of the drone defense system.

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Publication Details

Published
2024-09-27
DOI
https://doi.org/10.1109/aaac63570.2024.11027396
Primary Topic
UAV Applications and Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Design of the "Sky Watcher" Intelligent Anti-Drone System

Shangyu Gong, Oleg Olegovich Varlamov, Qinqin Shen
UAV Applications and Optimization
article

Design of the "Sky Watcher" Intelligent Anti-Drone System

Shangyu Gong, Oleg Olegovich Varlamov, Qinqin Shen
article en

Abstract

In recent years, the rapid development of drone technology and the reduction in manufacturing costs have led to a significant increase in the number of drones. Consequently, illegal intrusions or accidental entries by drones have resulted in infringements of personal privacy and threats to public safety. Therefore, it has become urgent to develop effective anti-drone systems. This paper proposes a computer vision-based drone defense detection system that not only detects drones but also distinguishes between drones and birds, addressing the limitations of traditional radar and infrared detection technologies in target identification. Additionally, this paper creates a "drone-bird" dataset and further enhances it through data augmentation techniques, improving the model's generalization and recognition capabilities for targets of different sizes and poses. Moreover, a dynamic detection head based on the attention mechanism is introduced into the model, significantly enhancing the detection efficiency and accuracy of small drones, thereby improving the performance of the drone defense system.

Bauman Moscow State Technical University (RU)
China Scholarship Council
Affordable and clean energy
Openalex Percentile: Top 51%
UAV Applications and Optimization
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