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融合重参数化与注意力机制的水下视觉多目标跟踪算法

李军毅 何铭乐 刘畅 徐雍

李军毅, 何铭乐, 刘畅, 等. 融合重参数化与注意力机制的水下视觉多目标跟踪算法[J]. 水下无人系统学报, xxxx, x(x): x-xx doi: 10.11993/j.issn.2096-3920.2025-0012
引用本文: 李军毅, 何铭乐, 刘畅, 等. 融合重参数化与注意力机制的水下视觉多目标跟踪算法[J]. 水下无人系统学报, xxxx, x(x): x-xx doi: 10.11993/j.issn.2096-3920.2025-0012
LI Junyi, HE Mingle, LIU Chang, XU Yong. Underwater Visual Multiple Object Tracking Algorithm Integrating Re-parameterization and Attention Mechanism[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0012
Citation: LI Junyi, HE Mingle, LIU Chang, XU Yong. Underwater Visual Multiple Object Tracking Algorithm Integrating Re-parameterization and Attention Mechanism[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0012

融合重参数化与注意力机制的水下视觉多目标跟踪算法

doi: 10.11993/j.issn.2096-3920.2025-0012
基金项目: 国家自然科学基金项目资助(62206063、62121004、U22A2044); 广东省基础与应用基础研究基金项目(2024A1515010369).
详细信息
    作者简介:

    李军毅(1991-), 男, 博士, 助理研究员, 主要研究方向为水下无人自主系统控制理论与应用

  • 中图分类号: TJ630.34; U674.76

Underwater Visual Multiple Object Tracking Algorithm Integrating Re-parameterization and Attention Mechanism

  • 摘要: 复杂的水下环境会严重影响成像设备的稳定性和获取图像的质量, 从而给水下无人自主系统视觉多目标跟踪带来极大挑战。为了解决水下相机抖动和图像退化带来的困难, 文中提出一种适用于水下无人自主系统的融合重参数化与注意力机制的水下视觉多目标跟踪算法。首先, 针对水下目标多样及图像退化等问题, 提出基于重参数化和注意力机制改进的YOLOv8 算法(RA-YOLOv8), 通过融合结构重参数化的多尺度特征提取卷积结构(DBB-RFAConv)和注意力机制, 有效增强网络的多尺度特征提取能力和提高模型的检测精度; 然后, 针对水下相机抖动问题给目标实时跟踪带来的挑战, 提出基于Inner-PIoUv2 改进的ByteTrack算法(IP2-ByteTrack), 使用 Inner-PIoUv2 作为跟踪算法匹配过程中的相似度度量, 增强模型在水下检测和跟踪任务中的性能, 提高跟踪轨迹匹配准确性; 最后, 基于RA-YOLOv8 和IP2-ByteTrack 算法, 提出一种用于水下无人自主系统的融合重参数化与注意力机制的水下视觉多目标跟踪算法。实验结果表明, 所提算法在复杂水下环境中表现出优异的性能, 能够有效解决现有方法在水下多目标跟踪中的不足。

     

  • 图  1  DBB-RFAConv的网络结构图

    Figure  1.  Network architecture diagram of DBB-RFAConv

    图  2  AMSCE-Attention的网络结构图

    Figure  2.  Network architecture diagram of AMSCE-Attention

    图  3  改进后的YOLOv8的网络结构图

    Figure  3.  Network structure diagram of improved yolov8

    图  4  Inner-PIoUv2的结构图

    Figure  4.  The structure diagram of Inner-PIoUv2

    图  5  水下多目标跟踪算法完整流程图

    Figure  5.  Complete flow chart of underwater multi-target tracking algorithm

    图  6  训练阶段和验证阶段中的损失函数和评估指标图

    Figure  6.  Chart of loss functions and evaluation metrics during training and validation phases

    图  7  可视化检测结果对比图

    Figure  7.  Comparison chart of visual test results

    图  8  可视化跟踪结果对比图

    Figure  8.  Comparison chart of visual tracking results

    表  1  实验环境配置

    Table  1.   The configuration of the experimental environment

    名称 版本
    CPU Intel 13th Gen i9-13900HX
    GPU NVIDIA GeForce RTX 4060
    内存 64G
    操作系统 Windows11
    Python 3.11
    下载: 导出CSV

    表  2  不同YOLO系列模型性能对比实验结果

    Table  2.   Experimental results of performance comparison of different Yolo series models

    模型精确度召回率mAP50mAP50-95FPS
    YOLOv781.5%72.0%75.0%31.6%35.46
    YOLOv883.2%73.5%82.1%36.6%35.58
    YOLOv1072.0%71.7%77.5%36.4%35.84
    下载: 导出CSV

    表  3  DBB-RFAConv的消融试验实验结果

    Table  3.   The experimental results of ablation studies on DBB-RFAConv

    模型 精确度 召回率 mAP50 mAP50-95 GFLOPs 参数量
    YOLOv8
    (Baseline)
    83.2% 73.5% 82.1% 36.6% 8.1 3 005 843
    YOLOv8 +
    RFAConv
    84.9% 72.9% 82.2% 37.4% 8.2 3 016 211
    YOLOv8 + DBB-
    RFAConv
    84.3% 75.8% 85.4% 37.6% 8.2 3 017 075
    下载: 导出CSV

    表  4  不同注意力的消融实验结果

    Table  4.   The experimental results of ablation studies on different types of attention

    模型 精确度 召回率 mAP50 mAP50-95 GFLOPs
    YOLOv8 + DBB-
    RFAConv(Baseline)
    84.3% 75.8% 85.4% 37.6% 8.2
    Baseline + SimAM 82.7% 72.3% 82.7% 36.6% 8.2
    Baseline + CBAM 82.9% 75.7% 83.6% 37.5% 8.2
    Baseline + DHSA 87.8% 72.4% 83.2% 37.6% 8.5
    Baseline + EMA 87.9% 79.1% 85.9% 37.8% 8.2
    Baseline + MAB 83.1% 74.9% 82.3% 37.4% 8.6
    Baseline + MSCA 87.0% 75.2% 83.8% 37.9% 8.4
    Baseline + CPCA 86.4% 77.3% 83.5% 38.2% 8.3
    Baseline + AMSCE-
    Attention(Ours)
    89.4% 77.0% 86.2% 38.8% 8.2
    下载: 导出CSV

    表  5  不同多目标跟踪算法的实验结果

    Table  5.   Experimental results of different multi-object tracking algorithms

    模型IDF1IDPIDRIDsMOTAMOTPFPS
    YOLOv8 + ByteTrack65.5%86.2%52.8%21.454%0.38724.92
    YOLOv8 + BoT-SORT69.68%90.64%56.59%14.560.6%0.43110.02
    YOLOv8 + OC-SORT60.6%85.3%47.2%22.249.2%0.34224.11
    YOLOv8 + DeepOC-SORT60.6%85.5%47.1%21.449.1%0.33923.18
    下载: 导出CSV

    表  6  多目标跟踪算法的消融实验结果

    Table  6.   Ablation experiment results of multi-object tracking algorithms

    模型 IDF1 IDP IDR IDs MOTA MOTP FPS
    YOLOv8 + ByteTrack(Baseline) 65.5% 86.2% 52.8% 21.4 54% 0.387 24.92
    YOLOv8 + DBB-RFAConv+AMSCEA 70.0% 87.4% 58.4% 18.4 58.7% 0.402 24.24
    YOLOv8 + DBB-RFAConv + AMSCE-Attention + InnerPIoUv2(Ours) 71.5% 88.9% 59.8% 15.3 59.3% 0.393 24.05
    下载: 导出CSV
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出版历程
  • 收稿日期:  2025-01-15
  • 修回日期:  2025-02-20
  • 录用日期:  2025-02-25
  • 网络出版日期:  2025-03-10

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