Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (9): 2893-2899.DOI: 10.11772/j.issn.1001-9081.2021071286

• Multimedia computing and computer simulation • Previous Articles    

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.


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
  • 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;


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



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

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