《计算机应用研究》|Application Research of Computers

级联特征点检测的锥套小目标快速检测定位算法

Fast detection and location algorithm for small drogue based on cascaded feature point detection

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作者 李旺灵,孙永荣,曾庆化,王国屹,赵伟
机构 南京航空航天大学 自动化学院 导航研究中心,南京 211106
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文章编号 1001-3695(2020)06-056-1871-05
DOI 10.19734/j.issn.1001-3695.2018.12.0920
摘要 为提升无人机自主空中加油中锥套小目标的检测精度和实时性,提出了级联网络与特征点检测网络的锥套小目标高精度快速定位算法。该算法设计了锥套目标全局粗定位和局部精定位的两级检测神经网络结构,采用特征点的输出位置误差及特征点群拟合的椭圆参数误差设计网络的损失函数,利用特征点拟合的锥套目标尺寸位置信息修正跟踪算法,提升了跟踪算法的和目标定位的实时性。实验测试结果表明,该定位算法的定位成功率在95%以上,定位的精度(预测区域与真实区域的重叠率)在80%以上,定位输出速率达到了100 Hz,对于环境的变化有着较强的适应性。因此该算法可以快速准确地进行锥套目标的跟踪,对于无人机空中加油技术的发展具有重要的研究意义。
关键词 深度学习; 级联检测; 特征点检测; 跟踪定位
基金项目 江苏省政策引导类计划基金资助项目(BY2016003-16)
本文URL http://www.arocmag.com/article/01-2020-06-056.html
英文标题 Fast detection and location algorithm for small drogue based on cascaded feature point detection
作者英文名 Li Wangling, Sun Yongrong, Zeng Qinghua, Wang Guoyi, Zhao Wei
机构英文名 Navigation Research Center,College of Automation Engineering,Nanjing University of Aeronautics & Astronautics,Nanjing 211106,China
英文摘要 In order to improve the detection accuracy and real-time performance of small drogue in autonomous aerial refueling of UAV, this paper proposed a fast and accurate location algorithm for small drogue based on cascade network and feature point detection network. It designed a two-stage detection neural network structure for global rough localization and local precise localization of drogue, and used the output position error of feature points and the elliptic parameter error of feature group fitting to design the loss function of the network. The method used the size and location of the drogue to correct tracking algorithm. The experimental results show that the proposed algorithm has a successful localization rate of more than 95%, a localization accuracy of more than 80%, and a localization output rate of 100 Hz. It has a strong adaptability to the changes of the environment. In summary, the algorithm can track the drogue target quickly and accurately, and has important research significance for the development of UAV aerial refueling technology.
英文关键词 deep learning; cascade detection; feature points detection; tracking and location
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收稿日期 2018/12/27
修回日期 2019/2/26
页码 1871-1875
中图分类号 TP391.41
文献标志码 A