低空无人机探测增强数据集研究

Enhancement Dataset for Low Altitude Unmanned Aerial Vehicle Detection

近年来,与无人机有关的事故显著增加,使得对反无人机系统研究有着迫切的需求。在对低空无人机探测的方法中深度学习算法应用具有较大潜力,但目前缺乏高质量的低空无人机探测数据集。本文研究建立了一种包含无人机和其他干扰目标的综合增强高质量数据集。通过实际场景拍摄、网络爬虫和数据增强方法获得大量高清无人机图像。并且,为解决复杂场景和远距离的低空无人机探测以及干扰目标所造成的较高误检率和漏检率问题,在数据集中增加了具有干扰特征的目标。最后,通过4种主流深度学习模型对所建立的数据集进行训练、验证和测试。结果表明,建立的高质量数据集可显著提高无人机探测的准确率。该研究为反无人机系统研究以及无人机探测模型优化提供了一定的验证数据集。

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.