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基于双注意力混洗的无人机航拍目标跟踪算法

金国栋 薛远亮 谭力宁 许剑锟

金国栋,薛远亮,谭力宁,等. 基于双注意力混洗的无人机航拍目标跟踪算法[J]. 北京航空航天大学学报,2023,49(1):53-65 doi: 10.13700/j.bh.1001-5965.2021.0177
引用本文: 金国栋,薛远亮,谭力宁,等. 基于双注意力混洗的无人机航拍目标跟踪算法[J]. 北京航空航天大学学报,2023,49(1):53-65 doi: 10.13700/j.bh.1001-5965.2021.0177
JIN G D,XUE Y L,TAN L N,et al. Aerial object tracking algorithm for UAVs based on dual-attention shuffling[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(1):53-65 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0177
Citation: JIN G D,XUE Y L,TAN L N,et al. Aerial object tracking algorithm for UAVs based on dual-attention shuffling[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(1):53-65 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0177

基于双注意力混洗的无人机航拍目标跟踪算法

doi: 10.13700/j.bh.1001-5965.2021.0177
基金项目: 国家自然科学基金 (61673017,61403398)
详细信息
    作者简介:

    金国栋等:基于双注意力混洗的多尺度无人机航拍目标跟踪算法 13

    通讯作者:

    E-mail:641797825@qq.com

  • 中图分类号: V279;TP391

Aerial object tracking algorithm for UAVs based on dual-attention shuffling

Funds: National Natural Science Foundation of China (61673017,61403398)
More Information
  • 摘要:

    针对无人机(UAV)跟踪过程中目标经常出现尺寸小、尺度变化大和相似物干扰等问题,提出了一种基于双注意力混洗的多尺度无人机实时跟踪算法。考虑到无人机视角下目标像素点少,构建了双采样融合的深层网络,既提供了语义信息丰富的深度特征,又保留了目标的细节信息;设计了双注意力混洗模块,通道注意力和空间注意力同时分组筛选提取到的特征信息,混洗不同通道间的信息,加强信息交流,提高了算法辨别能力;为利用不同层的特征信息,加入多个区域建议网络完成目标的分类和回归,并针对无人机的目标特点,将结果进行加权融合。实验结果表明:所提算法在数据集上的成功率和准确率分别为60.3%和79.3%,速度为37.5 帧/s。所提算法的辨别能力和多尺度适应能力明显增强,能有效应对无人机跟踪中常见的挑战。

     

  • 图 1  残差模块

    Figure 1.  ResBlock

    图 2  本文算法网络结构

    Figure 2.  Structure of the proposed algorithm network

    图 3  投影映射的下采样

    Figure 3.  Down-sampling of projection mapping

    图 4  步长为2、大小为1的卷积过程

    Figure 4.  Convolution process with a step of 2 and size of 1

    图 5  双注意力混洗

    Figure 5.  Dual-attention shuffling

    图 6  通道混洗

    Figure 6.  Channel shuffle

    图 7  Grad-CAM热力图

    Figure 7.  Heatmap of Grad-CAM

    图 8  RPN模块

    Figure 8.  Region proposal network module

    图 9  算法实现步骤

    Figure 9.  Algorithm flowchart

    图 10  部分跟踪结果

    Figure 10.  Some tracking results

    图 11  不同算法在UAV123数据集上的整体评估结果

    Figure 11.  Overall evaluation results of different algorithms on UAV123 dataset

    不同算法在UAV123数据集上各属性的评估结果

    Evaluation results of different algorithms in terms of different attributes of UAV123 dataset

    图 13  无人机拍摄视频跟踪结果

    Figure 13.  Tracking results of UAV shooting video

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出版历程
  • 收稿日期:  2021-04-07
  • 录用日期:  2021-05-28
  • 网络出版日期:  2023-01-16
  • 刊出日期:  2021-06-16

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