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基于图诱导对齐的域自适应水下目标检测方法

刘麒东 沈鑫 刘海路 丛璐 付先平

刘麒东, 沈鑫, 刘海路, 等. 基于图诱导对齐的域自适应水下目标检测方法[J]. 水下无人系统学报, xxxx, x(x): x-xx doi: 10.11993/j.issn.2096-3920.2023-0149
引用本文: 刘麒东, 沈鑫, 刘海路, 等. 基于图诱导对齐的域自适应水下目标检测方法[J]. 水下无人系统学报, xxxx, x(x): x-xx doi: 10.11993/j.issn.2096-3920.2023-0149
LIU Qidong, SHEN Xin, LIU Hailu, CONG Lu, FU Xianping. GPA based domain adaptive feature refinement method for underwater target detection[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0149
Citation: LIU Qidong, SHEN Xin, LIU Hailu, CONG Lu, FU Xianping. GPA based domain adaptive feature refinement method for underwater target detection[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0149

基于图诱导对齐的域自适应水下目标检测方法

doi: 10.11993/j.issn.2096-3920.2023-0149
详细信息
    作者简介:

    刘麒东(2000-),男,在读硕士,主要研究方向为目标检测

  • 中图分类号: TP391.41

GPA based domain adaptive feature refinement method for underwater target detection

  • 摘要: 水下环境因受到光照、泥沙等影响, 在水下进行目标检测时通常更容易出现域偏移而导致检测精度下降。针对此现象, 文中提出了基于图诱导对齐的域自适应水下目标检测方法, 图诱导原型对齐(GPA)是通过区域建议之间基于图的信息传播得到图像中的实例级特征, 再导出每个类别的原型表示用于类别级域的对齐。通过上述操作可以很好地聚合水下目标不同的模态信息, 以此实现源域和目标域的对齐从而减少域偏移带来的影响。此外, 为了使神经网络专注于不同水域分布下的实例级特征, 还在其中添加了卷积块注意(CBAM)模块。实验结果证明, 水下环境中GPA能有效对齐源域和目标域中的实例特征, CBAM可以使网络更加注意图像中的实例特征, 提高检测精度。

     

  • 图  1  水下图像的域偏移现象

    Figure  1.  Domain shift phenomenon in underwater images

    图  2  GPA检测框架

    Figure  2.  GPA testing framework

    图  3  SEM结构

    Figure  3.  SEM structure

    图  4  DRAW结构

    Figure  4.  DRAW structural diagram

    图  5  CBAM概述

    Figure  5.  Overview of CBAM

    图  6  礁石地形注意力可视化

    Figure  6.  Reef terrain attention visualization

    图  7  沙石地形注意力可视化

    Figure  7.  Sand and stone terrain visualization attention

    图  8  实验检测结果

    Figure  8.  Experimental detection result

    表  1  水下数据集URPC2020->HMRD实验结果(%)

    Table  1.   Underwater dataset URPC2020->HMRD experimental results(%)

    方法海参海胆海星平均精度
    基线41.762.347.450.3
    GPA方法48.167.954.756.9
    DA方法49.268.158.658.6
    本方法52.069.760.160.6
    下载: 导出CSV

    表  2  公共数据集VOC12->VOC07实验结果(%)

    Table  2.   Public dataset VOC12->VOC07 experimental results(%)

    方法自行车轿车沙发火车平均精度
    基线73.769.372.177.280.076.260.188.374.6
    GPA方法76.068.174.275.384.277.861.885.975.4
    DA方法77.075.082.179.383.376.071.477.677.7
    本方法74.568.674.677.183.477.663.588.576.0
    下载: 导出CSV

    表  3  公共数据集sim10K->cityscapes实验结果(%)

    Table  3.   Public dataset sim10K->Cityscapes experimental results(%)

    方法精度
    基线34.9
    GPA方法46.1
    DA方法45.8
    本方法48.3
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-11-22
  • 修回日期:  2024-01-01
  • 录用日期:  2024-01-05
  • 网络出版日期:  2024-02-01

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