基于轻量孪生网络的无人机目标跟踪算法

钟晓伟; 王志胜; 丛玉华

doi:10.15892/j.cnki.djzdxb.2023.05.004

弹箭与制导学报 >

2023 , Vol. 43 >Issue 5: 25 - 33

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2023.05.004

基于轻量孪生网络的无人机目标跟踪算法

钟晓伟 ,
王志胜 ,
丛玉华

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南京航空航天大学自动化学院,江苏南京 211106

钟晓伟(1998—),男,硕士研究生,研究方向:无人机视觉跟踪。

收稿日期: 2023-06-01

网络出版日期: 2024-12-30

基金资助

国家自然科学基金(62106104)

江苏高校哲学社会科学研究项目(2021SJA2.250)

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Unmanned Aerial Vehicle Target Tracking Algorithm Based on Lightweight Siamese Networks

ZHONG Xiaowei ,
WANG Zhisheng ,
CONG Yuhua

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School of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China

Received date: 2023-06-01

Online published: 2024-12-30

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摘要

针对视觉目标跟踪在平衡算法跟踪性能和实时性方面存在的难点,提出了一种基于轻量化设计的孪生网络目标跟踪算法SiamLD。对主干网络进行轻量化设计,降低参数量和运算量,提高算法实时性。通过高低层特征融合模块,增强对低层语义信息的利用,并利用多分支交叉相关和完全交并比方法,提升了跟踪算法的跟踪性能。在UAV123和DTB70跟踪基准上的实验结果表明,SiamLD算法跟踪性能优于其他主流算法,且在无人机平台中运行速度达到41FPS,有效地平衡了算法跟踪性能和实时性。

关键词： 无人机; 目标跟踪; 孪生网络; 轻量化; 特征融合

本文引用格式

钟晓伟 , 王志胜 , 丛玉华 . 基于轻量孪生网络的无人机目标跟踪算法[J]. 弹箭与制导学报, 2023 , 43(5) : 25 -33 . DOI: 10.15892/j.cnki.djzdxb.2023.05.004

Abstract

Aiming to address the difficulties in balancing tracking performance and real-time capability in visual object tracking, a lightweight twin-network object tracking algorithm called SiamLD is proposed. The main network is designed to be lightweight to reduce the number of parameters and computation, thus improving the real-time performance of the algorithm. In addition, a high-low-level feature fusion module is used to enhance the utilization of low-level semantic information, and the tracking performance is improved by using multi-branch cross-correlation and fully intersect-and-union method. Experimental results on the UAV123 and DTB70 tracking benchmarks show that the SiamLD algorithm outperforms other mainstream algorithms, and it runs at a speed of 41FPS on unmanned aerial vehicle platforms, effectively balancing tracking performance and real-time capability.

Key words： UAV; target tracking; siamese network; lightweight; feature fusion

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