计算机科学与探索2026，Vol.20Issue(1)：40-65,26.DOI:10.3778/j.issn.1673-9418.2506046
基于深度学习的无人机单目标跟踪综述
Survey of Deep Learning-Based UAV Single Object Tracking
摘要
Abstract
Deep learning-based UAV(unmanned aerial vehicle)single object tracking has emerged as a critical research area in computer vision,aiming to accurately track designated targets in aerial video sequences.UAV tracking presents unique challenges,including drastic perspective changes,variable target scales,and computational constraints.This survey system-atically categorizes recent methods into three technical approaches:traditional Siamese networks,CNN-Transformer hybrid architectures,and full Transformer methods,focusing on advances from 2022 to 2025.This paper proposes innovative sub-classifcation frameworks,including:module replacement,feature post-fusion,and collaborative modeling for CNN-Transformer hybrid architectures;static computation,hybrid mechanisms,and dynamic computation for single-stream Transformer methods.These frameworks reveal the evolution from performance-oriented to efficiency-performance balanced optimization.Comprehensive evaluations on UAV123,DTB70,UAVDT,and VisDrone2018 datasets validate the ad-vantages and limitations of different approaches.This paper identifies key challenges with future directions and engi-neering deployment guidance.关键词
无人机/单目标跟踪/深度学习/Siamese网络/TransformerKey words
unmanned aerial vehicle/single object tracking/deep learning/Siamese network/Transformer分类
信息技术与安全科学引用本文复制引用
陈泷,石磊,黎智辉,丁锰,潘亦伦..基于深度学习的无人机单目标跟踪综述[J].计算机科学与探索,2026,20(1):40-65,26.基金项目
中央高校基本科研业务费专项资金(2023JKF01ZK05).This work was supported by the Fundamental Research Funds for the Central Universities of China(2023JKF01ZK05). （2023JKF01ZK05）
