Air-to-Air Visual Detection of Micro-UAVs: An Experimental Evaluation of Deep Learning

This letter studies the problem of air-to-air visual detection of micro unmanned aerial vehicles (UAVs) by monocular cameras. This problem is important for many applications such as vision-based swarming of UAVs, malicious UAV detection, and see-and-avoid systems for UAVs. Although deep learning met...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2021-04, Vol.6 (2), p.1020-1027
Main Authors: Zheng, Ye, Chen, Zhang, Lv, Dailin, Li, Zhixing, Lan, Zhenzhong, Zhao, Shiyu
Format: Article
Language:English
Subjects:
Algorithms
Cameras
Datasets
Deep learning
Feature extraction
Image acquisition
Machine learning
Object detection
Object recognition
Swarming
Task analysis
UAV detection
Unmanned aerial vehicles
visual detection
Visual tasks
Visualization
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Summary:This letter studies the problem of air-to-air visual detection of micro unmanned aerial vehicles (UAVs) by monocular cameras. This problem is important for many applications such as vision-based swarming of UAVs, malicious UAV detection, and see-and-avoid systems for UAVs. Although deep learning methods have exhibited superior performance in many object detection tasks, their potential for UAV detection has not been well explored. As the first main contribution of this letter, we present a new dataset, named Det-Fly, which consists of more than 13 000 images of a flying target UAV acquired by another flying UAV. Compared to the existing datasets, the proposed one is more comprehensive in the sense that it covers a wide range of practical scenarios with different background scenes, viewing angles, relative distance, flying altitude, and lightning conditions. The second main contribution of this letter is to present an experimental evaluation of eight representative deep-learning algorithms based on the proposed dataset. To the best of our knowledge, this is the first comprehensive experimental evaluation of deep learning algorithms for the task of visual UAV detection so far. The evaluation results highlight some key challenges in the problem of air-to-air UAV detection and suggest potential ways to develop new algorithms in the future. The dataset is available at https://github.com/Jake-WU/Det-Fly .
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3056059