文献类型：期刊文献

中文题名：低空无人机探测增强数据集研究

英文题名：Enhancement Dataset for Low Altitude Unmanned Aerial Vehicle Detection

作者：Wang, Zhi[1,1,2,2]; Hu, Wei[3,3]; Wang, Ershen[3,3]; Hong, Chen[4,4]; Xu, Song[3,3]; Liu, Meizhi[5,5]

第一作者：Wang, Zhi

机构：[1] Zhejiang Jiande General Aviation Research Institute, Jiande, 311612, China; [2] Department of General Aviation, Civil Aviation Management Institute of China, Beijing, 100102, China; [3] School of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang, 110136, China; [4] 4. College of Robotics, Beijing Union University, Beijing, 100101, China; [5] School of Artificial Intelligence, Shenyang Aerospace University, Shenyang, 110136, China

第一机构：Zhejiang Jiande General Aviation Research Institute, Jiande, 311612, China

年份：2021

卷号：38

期号：6

起止页码：914-926

外文期刊名：Transactions of Nanjing University of Aeronautics and Astronautics

收录：EI(收录号：20220411508974)

语种：中文

外文关键词：Aircraft detection - Jamming - Learning systems - Antennas - Deep learning - Image enhancement - Unmanned aerial vehicles (UAV) - Web crawler

摘要：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. ? 2021, Editorial Department of Transactions of NUAA. All right reserved.