《计算机应用》唯一官方网站 ›› 2022, Vol. 42 ›› Issue (9): 2893-2899.DOI: 10.11772/j.issn.1001-9081.2021071286

• 多媒体计算与计算机仿真 • 上一篇    

空中加油场景下的目标联合检测跟踪算法

张怡(), 孙永荣, 赵科东, 李华, 曾庆化   

  1. 南京航空航天大学 导航研究中心,南京 210016
  • 收稿日期:2021-07-16 修回日期:2021-09-07 接受日期:2021-09-14 发布日期:2021-09-27 出版日期:2022-09-10
  • 通讯作者: 张怡
  • 作者简介:孙永荣(1969—),男,江苏海安人,教授,博士,主要研究方向:惯性导航与组合导航、视觉导航、航空电子系统及控制;
    赵科东(1993—),男,浙江诸暨人,博士研究生,主要研究方向:视觉导航;
    李华(1996—),男,江苏南通人,硕士研究生,主要研究方向:视觉导航;
    曾庆化(1979—),男,江苏连云港人,教授,博士,主要研究方向:惯性/卫星/视觉/无线传感器的定位与导航、多信息数据智能融合。

Joint detection and tracking algorithm of target in aerial refueling scenes

Yi ZHANG(), Yongrong SUN, Kedong ZHAO, Hua LI, Qinghua ZENG   

  1. Navigation Research Center,Nanjing University of Aeronautics and Astronautics,Nanjing Jiangsu 210016,China
  • Received:2021-07-16 Revised:2021-09-07 Accepted:2021-09-14 Online:2021-09-27 Published:2022-09-10
  • Contact: Yi ZHANG
  • About author:SUN Yongrong, born in 1969, Ph. D., professor. His research interests include inertial navigation and integrated navigation, visual navigation, avionics system and control.
    ZHAO Kedong, born in 1993, Ph. D. candidate. His research interests include visual navigation.
    LI Hua, born in 1996, M. S. candidate. His research interests include visual navigation.
    ZENG Qinghua, born in 1979, Ph. D., professor. His research interests include inertial/satellite/visual/wireless sensor positioning and navigation, intelligent fusion of multi-information data.

摘要:

针对自主空中加油对接阶段的目标跟踪问题,提出一种空中加油场景下的目标联合检测跟踪算法。该算法采用检测跟踪一体化的CenterTrack网络实现对锥套的追踪,而针对计算量较大、训练耗时过长的问题,分别从模型设计与网络优化两方面改善该网络。首先,在跟踪器中引入膨胀卷积组,以在不改变感受野大小的前提下使得网络轻量化;同时,将输出部分的卷积层替换为深度可分离卷积层,从而减少网络的参数量与计算量;然后,对网络进行进一步的优化,即将随机梯度下降(SGD)法与Adam算法相结合,使网络更快收敛至稳定状态;最后,利用真实的空中加油场景视频与地面模拟视频制作相应格式的数据集,并将其用于实验验证。分别在自制的锥套数据集和MOT17公共数据集上进行了训练与测试,证实了提出算法的有效性。相较于原CenterTrack网络,改进的网络Tiny-CenterTrack减少了约48.6%的训练时长,并在实时性方面提升了8.8%。实验结果表明,改进后的网络在不损失网络性能的前提下可有效节省计算资源并在一定程度上提升实时性。

关键词: 空中加油, 检测跟踪一体化, 网络轻量化, 膨胀卷积, 深度可分离卷积, 网络优化

Abstract:

Focusing on the target tracking problem in the docking stage of autonomous aerial refueling, a joint detection and tracking algorithm of target in aerial refueling scenes was proposed. In the algorithm, CenterTrack network with integrated detection and tracking was adopted to track the drogue. In view of the large computational cost and long training time, this network was improved from two aspects: model design and network optimization. Firstly, dilated convolution group was introduced into the tracker to make the network weight lighter without changing the size of the receptive field. At the same time, the convolutional layer of the output part was replaced with depthwise separable convolutional layer to reduce the network parameters and computational cost. Then, the network was further optimized to make it converge to a stable state faster by combining Stochastic Gradient Descent (SGD) method with Adaptive moment estimation (Adam) algorithm. Finally, videos of real-world aerial refueling scenes and simulations on the ground were made into dataset with the corresponding format for experimental verification. The training and testing were carried out on the self-built drogue dataset and MOT17 (Multiple Object Tracking 17) public dataset respectively, and the effectiveness of the proposed algorithm was verified. Compared to the original CenterTrack network, the improved network Tiny-CenterTrack reduces training time by about 48.6% and improves the real-time performance by 8.8%. Experimental results show that the improved network can effectively save the computing resources and improve the real-time performance to a certain extent without the loss of network performance.

Key words: aerial refueling, integration of detection and tracking, network lightweight, dilated convolution, depthwise separable convolution, network optimization

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