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

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

刘孙相与^{1, 2,},
李贵涛^{1, 2,},
詹亚锋^{1, 2,},
高鹏^3,

1.
清华大学宇航中心北京 100084
2.
北京信息科学与技术国家研究中心北京 100084
3.
北京大学工学院北京 100871

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, 目标分割和深度学习. 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仿真和图像处理. 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TT&C系统, 信号处理和深空通信. E-mail: zhanyf@tsinghua.edu.cn

高鹏：北京大学工学院博士后. 主要研究方向为计算机体系结构, 机器学习和图像处理. 本文通信作者. E-mail: gaopeng1982@pku.edu.cn

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出版历程
- 收稿日期: 2020-10-14
- 网络出版日期: 2021-05-12
- 刊出日期: 2022-06-02

Drone Detection Based on Multi-order Kinematic Parameters

LIU Sun-Xiang-Yu^{1, 2
,},
LI Gui-Tao^{1, 2
,},
ZHAN Ya-Feng^{1, 2
,},
GAO Peng^3
,

1.
Space Center, Tsinghua University, Beijing 100084
2.
Beijing National Research Center for Information Science and Technology (BNRist), Beijing 100084
3.
College of Engineering, Peking University, Beijing 100871

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

More Information

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

LI Gui-Tao　Associate professor at School of Aerospace Engineering, 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 TT&C systems, communication signal processing, and deep space communications

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

摘要

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%.
- 四旋翼无人机 /
- 目标识别 /
- 运动特征 /
- 融合方法
Abstract: Due to the features of low, slow and small aircraft, such as quadrotors, it is a challenging and urgent problem to detect UAVs (Unmanned aerial vehicles) in the wild. Different from the past literatures directly using deep learning method, this paper exploits motion features by extracting multi-order kinematic parameters such as velocity, accelerate, angular velocity, angular velocity vectors and it is exposed that 2nd order and gravity direction motion parameters are key motion patterns for UAV detection. By building GBDT (Gradient boosting decision tree) and GRU (Gate recurrent unit) network, it comes out with a short-term and a long-term detection result, respectively. This recognition process integrates appearance detection result into motion detection result and obtains the final determination. The experimental results achieve state-of-the-art result, with a 103% increase on the precision index AP (Average precision) with respect to the previous work and a 26% increase for hybrid method.
- Quadrotors /
- object detection /
- motion feature /
- fusion method

HTML全文

图 1 本方法整体流程图

Fig. 1 The overall flowchart of our method

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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)

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图 3 运动区域提取示意图

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

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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

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图 5 运动目标区域提取结果图

Fig. 5 Extraction result of motion ROIs

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图 6 深度估计结果图

Fig. 6 Result of depth estimation

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图 7 运动参量估计误差箱图

Fig. 7 Boxplot for motion parameter error estimation

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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)

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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

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图 10 不同识别方法的性能对比图

Fig. 10 Comparison of performance for different detection methods

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图 11 训练得到的梯度提升树示意图

Fig. 11 A single tree from the trained GDBT

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图 12 不同参量组合的识别结果图

Fig. 12 Detection results of different parameter combinations

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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^[59]	NYU数据集^[60]
目标类别数	5	2	2	12	894
平均每类视频帧数	3000	1500	3000	3000	39
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	√	×	×	×	×

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表 2 MUD数据集采集设备说明

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, F 3.5 ~ 6.3	—
GPS	GPS/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}}$

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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]	413	0.832	0.201
混合高斯法^[27]	315	0.784	0.249
光流法^[61]	521	0.853	0.164
Vibe+法^[30]	238	0.868	0.365

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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^[51]	0 ~ 100 m	0.103	0.321	9.014	0.832	0.875	0.922
GeoNet^[63]	0 ~ 100 m	0.280	2.813	14.312	0.817	0.849	0.895
双目视觉^[64]	0 ~ 100 m	0.062	1.210	0.821	0.573	0.642	0.692
激光测距	0 ~ 200 m	0.041	2.452	1.206	0.875	0.932	0.961
DORN^[51]	200 ~ 500 m	0.216	1.152	13.021	0.672	0.711	0.748
GeoNet^[63]	200 ~ 500 m	0.398	5.813	18.312	0.617	0.649	0.696
双目视觉^[64]	200 ~ 500 m	0.786	5.210	25.821	0.493	0.532	0.562
激光测距	200 ~ 500 m	0.078	3.152	2.611	0.891	0.918	0.935

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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

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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	0.67	0.25	0.02	0.01	0.12
鸟类	0.21	0.58	0.01	0.00	0.10
行人	0.01	0.02	0.75	0.06	0.09
车辆	0.01	0.00	0.10	0.80	0.08
其他物体	0.10	0.15	0.12	0.13	0.61

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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	AP50	AP90
FlowNet^[33]	32.2	0.30	2.87	42.0	10.3
IRRCNN^[24]	36.7	0.37	10.18	50.9	7.2
Xiao^[42]	50.3	0.55	2.34	59.3	19.7
Faster RCNN^[18]	47.8	0.57	11.07	62.1	18.5
Luo^[41]	57.2	0.59	7.70	71.7	24.4
Rozantsev^[15]	62.1	0.65	14.10	81.3	37.2
本文非零阶参数方法(GRU)	65.6	0.78	5.34	79.5	39.8
本文多阶运动参量方法	78.5	0.80	6.54	91.2	46.8

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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

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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

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