在利用视觉检测算法进行检测与定位的过程中,当空域中目标无人机相对较小时,现有的检测算法容易受到空中其他飞行物、复杂背景和光照强度变化影响导致检测精度较低。为了解决这一问题,提出了一种基于YOLOv3改进的目标检测算法。当空域...在利用视觉检测算法进行检测与定位的过程中,当空域中目标无人机相对较小时,现有的检测算法容易受到空中其他飞行物、复杂背景和光照强度变化影响导致检测精度较低。为了解决这一问题,提出了一种基于YOLOv3改进的目标检测算法。当空域中目标无人机体积相对较小、视觉特征较弱、存在其他干扰时,通过增加主干特征提取网络对图像特征提取的层级数,提取多个不同尺度特征层进行跨层连接融合,使多个不同层级的特征层之间的语义信息联系得更加紧密,让网络模型可以学习到不同尺度目标的特征信息,以此增强检测算法对小目标无人机检测的精度。最后,利用Drone vs Birds数据集进行实验测试,所提出的算法可以有效地提高小型无人机目标的检测精度,检测速度基本满足实际要求。展开更多
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...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.展开更多
文摘在利用视觉检测算法进行检测与定位的过程中,当空域中目标无人机相对较小时,现有的检测算法容易受到空中其他飞行物、复杂背景和光照强度变化影响导致检测精度较低。为了解决这一问题,提出了一种基于YOLOv3改进的目标检测算法。当空域中目标无人机体积相对较小、视觉特征较弱、存在其他干扰时,通过增加主干特征提取网络对图像特征提取的层级数,提取多个不同尺度特征层进行跨层连接融合,使多个不同层级的特征层之间的语义信息联系得更加紧密,让网络模型可以学习到不同尺度目标的特征信息,以此增强检测算法对小目标无人机检测的精度。最后,利用Drone vs Birds数据集进行实验测试,所提出的算法可以有效地提高小型无人机目标的检测精度,检测速度基本满足实际要求。
基金supported by the National Natural Science Foundation of China(No. 62173237)the National Key R&D Program of China(No.2018AAA0100804)+7 种基金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)the Academic Research Projects of Beijing Union University(No.ZB10202005)。
文摘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.
