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基于语义特征的遥感舰船关重部位检测网络

张冬冬 王春平 付强

张冬冬, 王春平, 付强. 基于语义特征的遥感舰船关重部位检测网络[J]. 应用光学.
引用本文: 张冬冬, 王春平, 付强. 基于语义特征的遥感舰船关重部位检测网络[J]. 应用光学.
ZHANG Dongdong, WANG Chunping, FU Qiang. Remote sensing ship critical part detection network based on semantic feature[J]. Journal of Applied Optics.
Citation: ZHANG Dongdong, WANG Chunping, FU Qiang. Remote sensing ship critical part detection network based on semantic feature[J]. Journal of Applied Optics.

基于语义特征的遥感舰船关重部位检测网络

基金项目: 军内某科研项目(LJ20191A040155)
详细信息
    作者简介:

    张冬冬(1993—),男,硕士研究生,主要从事光电侦察情报处理、目标识别技术研究。E-mail:1042911849@qq.com

    通讯作者:

    付强(1981—),男,讲师,博士,主要从事智能视觉与目标检测技术研究。E-mail:1418748495@qq.com

  • 中图分类号: TP753

Remote sensing ship critical part detection network based on semantic feature

  • 摘要: 在近岸场景中,受背景影响,舰船关重部位误检概率高、检测精度低。针对以上问题,提出了一种基于语义特征的舰船关重部位检测网络,并命名为CPDNet(critical part detection network)。通过优化网络结构及引入注意力机制,提升网络的特征表达能力以及对关重部位的感知能力;基于语义信息,设计了语义掩膜模块,以降低背景对检测精度的影响;增加角度参数,使网络适用于具有方向性的目标;构建了舰船关重部位数据集,命名为CP-Ship,以验证所提网络的有效性。在CP-Ship数据集上的实验结果表明:所提网络的平均精度比RetinaNet提高了11.35%,与其他网络模型相比,在检测精度和速度均表现优异。
  • 图  1  CPDNet的整体架构

    Fig.  1  Overall architecture of the CPDNet

    图  2  FE-FPN结构图

    Fig.  2  The structure of FE-FPN

    图  3  CIEM结构图

    Fig.  3  The structure of CIEM

    图  4  SFEM结构图

    Fig.  4  The structure of SFEM

    图  5  标注示意图

    Fig.  5  Schematic diagram of labeling

    图  6  特征图可视化示例

    Fig.  6  Visualization examples of feature map

    图  7  不同网络模型的PR曲线

    Fig.  7  The PR curves of different network models

    图  8  不同网络模型的检测结果可视化示例

    Fig.  8  Visualization examples of detection results of different network models

    表  1  SFEM各层的详细参数

    Table  1  Detailed parameters of each SFEM layer

    卷积层通道数
    (输入)
    通道数
    (输出)
    大小
    (输入)
    大小
    (输出)
    Deconv 14 0962 048W×H2W×2H
    Conv 12 0481 0242W×2H2W×2H
    Deconv 22 0481 0242W×2H4W×4H
    Conv 21 0245124W×4H4W×4H
    Conv 35121284W×4H4W×4H
    Conv 4128644W×4H4W×4H
    Conv 56424W×4H4W×4H
    下载: 导出CSV

    表  2  CP-Ship数据集详细信息

    Table  2  Details of CP-Ship dataset

    数据集CP-Ship
    图像数量关重部位个数
    训练集8122 295
    测试集203561
    总计1 0152 856
    下载: 导出CSV

    表  3  膨胀结构元素尺寸选取

    Table  3  Selection of expansion structure element size

    结构元素尺寸AP/%结构元素尺寸AP/%
    3×368.6713×1371.38
    5×569.6915×1570.97
    7×770.2817×1769.94
    9×971.2519×1968.32
    11×1171.7621×2166.46
    下载: 导出CSV

    表  4  消融实验结果

    Table  4  Experiment results of ablation

    RetinaNet角度参数FE-FPNSMNAP/%
    60.41
    62.64
    66.45
    71.76
    下载: 导出CSV

    表  5  不同网络模型的定量结果

    Table  5  Quantiative results of different network models

    类别方法主干网络锚框类型TPFPFNAP/%FPS
    双阶段网络Faster R-CNN[9]ResNet-50水平框41115215065.7819.2
    ReDet[25]ResNet-50旋转框45410310775.243.1
    Dynamic R-CNN[26]ResNet-50水平框40013016163.8520.5
    Oriented R-CNN[27]ResNet-50旋转框42316913868.3911.8
    单阶段网络YOLOv3[28]DarkNet-53水平框4138414866.4951.8
    R3Det[15]ResNet-50旋转框39314616864.2811.3
    RetinaNet[13]ResNet-50水平框38722917460.4123.2
    CFA[29]ResNet-50旋转框41422530663.6119.3
    CPDNetResNet-50旋转框43315012871.7612.5
    下载: 导出CSV
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  • 网络出版日期:  2022-09-17

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