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空间目标自适应光学图像椭圆部件检测

寇鹏 智帅峰 程耘 刘永祥

寇鹏, 智帅峰, 程耘, 刘永祥. 空间目标自适应光学图像椭圆部件检测[J]. 中国光学(中英文), 2022, 15(3): 454-463. doi: 10.37188/CO.2021-0208
引用本文: 寇鹏, 智帅峰, 程耘, 刘永祥. 空间目标自适应光学图像椭圆部件检测[J]. 中国光学(中英文), 2022, 15(3): 454-463. doi: 10.37188/CO.2021-0208
KOU Peng, ZHI Shuai-feng, CHENG Yun, LIU Yong-xiang. Detection of elliptical components in adaptive optical image of space target[J]. Chinese Optics, 2022, 15(3): 454-463. doi: 10.37188/CO.2021-0208
Citation: KOU Peng, ZHI Shuai-feng, CHENG Yun, LIU Yong-xiang. Detection of elliptical components in adaptive optical image of space target[J]. Chinese Optics, 2022, 15(3): 454-463. doi: 10.37188/CO.2021-0208

空间目标自适应光学图像椭圆部件检测

doi: 10.37188/CO.2021-0208
基金项目: 国家自然科学基金(No. 61921001,No. 61801484)
详细信息
    作者简介:

    寇 鹏(1981—),男,河南洛阳人,博士研究生,西安卫星测控中心高级工程师,2003年于航天工程大学获得学士学位,2010年于国防科技大学获得硕士学位,主要从事空间目标探测与识别研究。E-mail:kou_810518@163.com

    智帅峰(1992—),男,河南偃师人,博士,讲师,2011年于国防科技大学获得学士学位,2017年于国防科技大学获得硕士学位,2021年于英国帝国理工大学获得博士学位,主要从事三维机器人视觉,智能信号处理等方面的研究。E-mail:zhishuaifeng11@nudt.edu.cn

    程 耘(1995—),男,重庆人,博士研究生,2018年于国防科技大学获得学士学位,主要从事阵列信号处理、电子对抗等方面的研究。E-mail:moraincy@126.com

    刘永祥(1976—),男,河北唐山人,博士,教授,1999年于国防科技大学获得学士学位,2004年于国防科技大学获得博士学位,主要研究方向为雷达信号处理与目标识别。E-mail:lyx_bible@sina.com

  • 中图分类号: TP391.4

Detection of elliptical components in adaptive optical image of space target

Funds: Supported by National Natural Science Foundation of China (No. 61921001, No. 61801484)
More Information
  • 摘要: 为了识别空间目标的椭圆部件,提出了一种基于自适应光学图像的椭圆检测方法。首先,利用RL(Richardson-Lucy)方法对自适应光学图像进行复原,在此基础上,采用弧支撑线段(Arc-Support Line Segments, ASLS)方法对复原图像进行椭圆检测。针对ASLS算法使用的Canny边缘提取算法带来的“弧段过分割”和“语义信息差”等问题,提出了基于多尺度组合分组(Multiscale Combinatorial Grouping, MCG)边缘提取的解决方法。最后,针对ASLS算法使用优度指标等验证方法存在部分虚假椭圆的情况,综合利用多种几何指标进行约束,有效地消除了虚假椭圆。实验结果表明:椭圆中心点检测误差优于3 pixels,半长轴误差优于4 pixels,方向角误差优于3°。在重叠面积门限为0.65时,本文算法的准确率为85.7%、召回率为93.3%,F值指标为0.893,优于传统椭圆检测算法。

     

  • 图 1  MCG算法流程图

    Figure 1.  Flow chart of the MCG algorithm

    图 2  空间目标自适应光学原始和边缘图像

    Figure 2.  Adaptive optics original and edge images of space target

    图 3  椭圆参数定义

    Figure 3.  Definition of ellipse parameters

    图 4  改进ASLS算法流程

    Figure 4.  Flow chart of the improved ASLS algorithm

    图 5  两个弧支撑组生成候选椭圆

    Figure 5.  Candidate ellipse generated by two arc-support groups

    图 6  空间目标自适应光学复原图像边缘提取结果

    Figure 6.  Edge extraction results of adaptive optics restoration image of space targets

    图 7  空间目标自适应光学复原图像椭圆检测结果

    Figure 7.  Ellipse detection results of adaptive optics restored images of partial space targets

    图 8  重叠面积门限与检测指标关系

    Figure 8.  Relationship between overlapping area threshold and detection index

    表  1  仿真图像椭圆参数平均误差

    Table  1.   Average error of linear structure components for test

    平均误差(像素)中心
    $ x $
    中心
    $ y $
    方向角
    $ \varphi $
    半长轴
    $ a $
    半短轴
    $ b $
    ELSDc40.1037.8147.26°44.6951.35
    AAMD2.8410.1310.33°13.4317.28
    ASLS2.575.082.11°6.684.32
    本文算法1.732.142.27°3.822.17
    下载: 导出CSV

    表  2  算法检测指标及平均耗时

    Table  2.   Average consumed times of those algorithms and the error detection rates

    ELSDcAAMDASLS本文算法
    准确率(%)28.651.769.185.7
    召回率(%)43.766.772.393.3
    F值0.4660.6410.7070.893
    平均耗时(s)10.0580.5250.65912.874
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-03
  • 录用日期:  2022-03-01
  • 修回日期:  2022-01-04
  • 网络出版日期:  2022-03-01
  • 刊出日期:  2022-05-20

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