• 人工智能与仿真 •

空中加油场景下的目标联合检测跟踪算法

1. 1. 南京航空航天大学自动化学院导航研究中心
2. 南京航空航天大学
• 收稿日期:2021-07-16 修回日期:2021-09-07 发布日期:2021-09-27 出版日期:2021-09-27
• 通讯作者: 张怡

Joint detection and tracking algorithm of target in aerial refueling

• Received:2021-07-16 Revised:2021-09-07 Online:2021-09-27 Published:2021-09-27

Abstract: Abstract: Focused on the issue of target tracking in the stage of autonomous aerial refueling, a joint detection and tracking algorithm of target in aerial refueling was proposed. CenterTrack network with integrated detection and tracking was adopted to track the drogue. In view of the large amount of calculation and long training time, this network was improved from two aspects: model design and network optimization. Dilation convolution group was firstly introduced into the tracker to make the network lighter without changing the size of the receptive field. At the same time, the convolutional layer of the output part was replaced with deeply separable convolutional layer to reduce the amount of network parameters and calculations. Moreover, 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). Finally, videos which shooted during aerial refueling process and simulated on the ground were made into corresponding format for experimental verification. Eventually, model were trained and tested respectively on the drogue dataset and MOT17(Multiple Object Tracking 17) dataset. The effectiveness of the algorithm has been verified. 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 computing resources and boost real-time performance to a certain extent without loss of network performance.