Transactions of Nanjing University of Aeronautics & Astronautics
Enhancement Dataset for Low Altitude Unmanned Aerial Vehicle Detection
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Abstract:
In recent years， the number of incidents involved with unmanned aerial vehicles （UAVs） has increased conspicuously， resulting in an increasingly urgent demand for developing anti-UAV systems. The vast requirements of high detection accuracy with respect to low altitude UAVs are put forward. In addition， the methods of UAV detection based on deep learning are of great potential in low altitude UAV detection. However， such methods need high-quality datasets to cope with the problem of high false alarm rate （FAR） and high missing alarm rate （MAR） in low altitude UAV detection， special high-quality low altitude UAV detection dataset is still lacking. A handful of known datasets for UAV detection have been rejected by their proposers for authorization and are of poor quality. In this paper， a comprehensive enhanced dataset containing UAVs and jamming objects is proposed. A large number of high-definition UAV images are obtained through real world shooting， web crawler， and data enhancement. Moreover， to cope with the challenge of low altitude UAV detection in complex backgrounds and long distance， as well as the puzzle caused by jamming objects， the noise with jamming characteristics is added to the dataset. Finally， the dataset is trained， validated， and tested by four mainstream deep learning models. The results indicate that by using data enhancement， adding noise contained jamming objects and images of UAV with complex backgrounds and long distance， the accuracy of UAV detection can be significantly improved. This work will promote the development of anti-UAV systems deeply，and more convincing evaluation criteria are provided for models optimization for UAV detection.
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Project Supported:
The work was supported by the National Natural Science Foundation of China （No.62173237）， the National Key R&D Program of China （No.2018AAA0100804）， the Zhejiang Key laboratory of General Aviation Operation technology（No.JDGA2020-7）， the Talent Project of Revitalization Liaoning （No.XLYC1907022）， the Key R & D Projects of Liaoning Province （No.2020JH2/10100045）， the Natural Science Foundation of Liaoning Province （No.2019-MS-251）， the Scientific Research Project of Liaoning Provincial Department of Education （No.JYT2020142）， the High-Level Innovation Talent Project of Shenyang （No.RC190030）， the Science and Technology Project of Beijing Municipal Commission of Education （No.KM201811417005），and the Academic Research Projects of Beijing Union University （No.ZB10202005）.
Clc Number:
TP391
CHINA AERO-SCIENCES
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