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基于多阶运动参量的四旋翼无人机识别方法

刘孙相与 李贵涛 詹亚锋 高鹏

刘孙相与, 李贵涛, 詹亚锋, 高鹏. 基于多阶运动参量的四旋翼无人机识别方法. 自动化学报, 2022, 48(6): 1−19 doi: 10.16383/j.aas.c200862
引用本文: 刘孙相与, 李贵涛, 詹亚锋, 高鹏. 基于多阶运动参量的四旋翼无人机识别方法. 自动化学报, 2022, 48(6): 1−19 doi: 10.16383/j.aas.c200862
Liu Sun-Xiang-Yu, Li Gui-Tao, Zhan Ya-Feng, Gao Peng. Drone detection based on multi-order kinematic parameters. Acta Automatica Sinica, 2022, 48(6): 1−19 doi: 10.16383/j.aas.c200862
Citation: Liu Sun-Xiang-Yu, Li Gui-Tao, Zhan Ya-Feng, Gao Peng. Drone detection based on multi-order kinematic parameters. Acta Automatica Sinica, 2022, 48(6): 1−19 doi: 10.16383/j.aas.c200862

基于多阶运动参量的四旋翼无人机识别方法

doi: 10.16383/j.aas.c200862
基金项目: 国家重点研发计划(2018YFD100303)资助
详细信息
    作者简介:

    刘孙相与:清华大学航天航空学院博士研究生. 主要研究方向为目标识别, 目标重建. E-mail: lsxy_qd@126.com

    李贵涛:清华大学航天航空学院副教授. 主要研究方向为计算机视觉, 大系统仿真, 数据可视化. E-mail: ligt@tsinghua.edu.cn

    詹亚锋:清华大学信息国家研究中心教授. 主要研究方向为微弱信号探测和飞行器测控. E-mail: zhanyf@tsinghua.edu.cn

    高鹏:北京大学工学院博士后. 主要研究方向为目标识别, 目标重建, 计算机体系结构. 本文通信作者. E-mail: gaopeng1982@pku.edu.cn

Drone Detection Based on Multi-order Kinematic Parameters

Funds: Supported by National Key Research and Development Program of China (2018YFD100303)
More Information
    Author Bio:

    Liu Sun-Xiang-Yu Ph. D. candidate at the School of Aerospace Engineering, Tsinghua University. His research interest covers object detection, object segmentation, and deep learning

    Li Gui-Tao Associate professor at Tsinghua University. His research interest covers computer simulation and image processing

    Zhan Ya-Feng Professor at Beijing National Research Center for Information Science and Technology, Tsinghua University. His research interest covers satellite TT&C systems, communication signal processing, and deep space communications

    GAO Peng Postdoctoral researcher at the College of Engineering, Peking University. His research interest covers computer architecture, machine learning, and image processing. Corresponding author of this paper

  • 摘要: 以小型多轴无人机为代表的低慢小目标, 通常难以被常规手段探测, 而此类目标又会严重威胁某些重要设施. 因此对该类目标的识别已经成为一个亟待解决的重要问题. 本文基于目标运动特征, 提出了一种无人机目标识别方法, 并揭示了二阶运动参量以及重力方向运动参量是无人机识别过程中的关键参数. 该方法首先提取候选目标的多阶运动参量, 建立梯度提升树(Gradient boosting decision tree, GBDT)和门控制循环单元(Gate recurrent unit, GRU)记忆神经网络分别完成短时和长期识别, 然后融合表观特征识别结果得到最终判别结果. 此外, 本文还建立了一个综合多尺度无人机数据集(Multi-scale UAV dataset, MUD), 本文所提出的方法在该数据集上相对于传统基于运动特征的方法, 其识别精度(Average precision, AP)提升103%, 融合方法提升26%.
  • 图  1  本方法整体流程图

    Fig.  1  The overall flowchart of our method

    图  2  基于多阶运动参量的目标识别方法流程图(MoKiP)

    Fig.  2  Flowchart of multi-order kinematic parameters based detection method (MoKiP)

    图  3  运动区域提取示意图

    Fig.  3  An illustration of the extracted motion ROI (Region of interest)

    图  4  本文实验所用数据集示意图

    Fig.  4  Illustration of parts of MUD used in our work

    图  5  运动目标区域提取结果图

    Fig.  5  Extraction result of motion ROIs

    图  6  深度估计结果图

    Fig.  6  Result of depth estimation in motion ROIs

    图  7  运动参量估计误差箱图

    Fig.  7  Boxplot for motion parameter error estimation

    图  8  不同参数组合的ROC曲线单参数变化时的ROC曲线(左中右分别为$D、M、J$单独变化)

    Fig.  8  ROC curves of different GBDT parameter combinations (the subplots from left to right are corresponding to D、M、J respectivly)

    图  9  基于运动参量决策树的无人机识别结果

    Fig.  9  Results of MoKiP by using GBDT

    图  10  不同识别方法的性能对比图

    Fig.  10  Comparison of performance for different detection methods

    图  11  训练得到的梯度提升树示意图

    Fig.  11  A single tree from the trained GDBT

    图  12  不同参量组合的识别结果图

    Fig.  12  Detection results of different parameter combinations

    表  1  本文所采集数据与其他运动目标数据集的对比

    Table  1  Comparison of different datasets for moving objects

    属性本文所采数据Drone-vs-Bird[23]运动相机数据集[15]Pascal3D+ 数据集[60]NYU数据集[61]
    目标类别数52212894
    平均每类视频帧数300015003000300039
    场景室内/室外室外室外室内/室外室内
    背景单一程度多背景单一单一多背景多背景
    姿态标注×××
    深度标注×××
    多视角覆盖××
    遮挡标注××
    位置姿态误差××××
    下载: 导出CSV

    表  2  主要采集设备及参数表

    Table  2  Main equipment for acquisition of multi-scale UAV dataset (MUD)

    设备参数精度
    相机SONY A7 ILCE-7M2, $6\,000 \times 4\,000$像素FE 24~240 mm, F3.5~6.3
    GPSGPS/GLONASS双模垂直$ \pm 0.5\;{\rm{m}}$, 水平$ \pm 1.5\;{\rm{m}}$
    激光测距仪SKIL Xact 0530, 0~80 m$ \pm 0.2\;{\rm{mm}}$
    下载: 导出CSV

    表  3  运动目标区域提取算法性能对比

    Table  3  Comparison between performance of different motion ROIs

    方法矩形框数量召回率单位召回率(每百个)
    帧差法[31]4130.8320.201
    混合高斯法[27]3150.7840.249
    光流法[64]5210.8530.164
    Vibe+法[30]2380.8680.365
    下载: 导出CSV

    表  4  不同深度估计方法误差对比

    Table  4  Error of different depth estimation methods

    方法探测范围绝对误差平方误差 均方根误差$\delta < 1.25$$\delta < {1.25^2}$$\delta < {1.25^3}$
    DORN[48]0 ~ 100 m0.1030.3219.0140.8320.8750.922
    GeoNet[62]0 ~ 100 m0.2802.81314.3120.8170.8490.895
    双目视觉[63]0 ~ 100 m0.0621.2100.8210.5730.6420.692
    激光测距0 ~ 200 m0.0412.4521.2060.8750.9320.961
    DORN[48]200 ~ 500 m0.2161.15213.0210.6720.7110.748
    GeoNet[62]200 ~ 500 m0.3985.81318.3120.6170.6490.696
    双目视觉[63]200 ~ 500 m0.7865.21025.8210.4930.5320.562
    激光测距200 ~ 500 m0.0783.1522.6110.8910.9180.935
    下载: 导出CSV

    表  5  图7中参数对照表

    Table  5  Illustrations of parameters in Fig. 7

    参数说明
    ${\boldsymbol v}$速度
    ${\boldsymbol a}$加速度
    ${\boldsymbol \omega}$ 角速度
    ${\boldsymbol \alpha }$角加速度
    X 轴分量方向与图像平面坐标系中 u 轴方向保持一致
    Y 轴分量方向与图像平面坐标系中 v 轴方向保持一致
    Z 轴分量方向铅垂向上
    下载: 导出CSV

    表  6  运动参量的决策树模型识别结果混淆矩阵

    Table  6  Confusion matrix of MokiP by using GDBT

    真实值
    预测值
    旋翼无人机鸟类行人车辆其他物体
    旋翼无人机0.670.250.020.010.12
    鸟类0.210.580.010.000.10
    行人0.010.020.750.060.09
    车辆0.010.000.100.800.08
    其他物体0.100.150.120.130.61
    下载: 导出CSV

    表  7  不同识别方法性能指标对比表

    Table  7  Comparison of performance indexes for different detection method

    方法AP精度95%转折点曲线尾部
    梯度
    AP50AP90
    FlowNet[33]32.20.302.8742.010.3
    IRRCNN[24]36.70.3710.1850.97.2
    Xiao[42]50.30.552.3459.319.7
    Faster RCNN[18]47.80.5711.0762.118.5
    Luo[41]57.20.597.7071.724.4
    Rozantsev[15]62.10.6514.1081.337.2
    本文非0阶参数
    方法(GRU)
    65.60.785.3479.539.8
    本文多阶运动
    参量方法
    78.50.806.5491.246.8
    下载: 导出CSV

    表  8  运动参量的性质对无人机识别的影响表

    Table  8  Impact of the parameter properties on UAV detection

    参量贡献度$D$平动参量旋转参量总贡献度
    一阶参量7.2%20.1%27.3%
    二阶参量34.1%38.6%72.7%
    总贡献度41.3%58.7%1
    下载: 导出CSV

    表  9  运动参量的方向对无人机识别的影响表

    Table  9  Impact of the parameter direction on UAV detection

    参量贡献度$D$沿X轴方向沿Y轴方向沿Z轴方向总贡献度
    平动参量8.3%8.8%24.2%41.3%
    旋转参量18.7%18.8%22.2%58.7%
    总贡献度27.0%27.6%46.4%1
    下载: 导出CSV
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  • 收稿日期:  2020-10-14
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