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面向舰船目标检测的SAR图像数据PCGAN生成方法

潘磊 郭宇诗 李恒超 王伟业 李泽琛 马天宇

潘磊, 郭宇诗, 李恒超, 王伟业, 李泽琛, 马天宇. 面向舰船目标检测的SAR图像数据PCGAN生成方法[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20210630
引用本文: 潘磊, 郭宇诗, 李恒超, 王伟业, 李泽琛, 马天宇. 面向舰船目标检测的SAR图像数据PCGAN生成方法[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20210630
PAN Lei, GUO Yushi, LI Hengchao, WANG Weiye, LI Zechen, MA Tianyu. SAR Image Generation Method via PCGAN for Ship Detection[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20210630
Citation: PAN Lei, GUO Yushi, LI Hengchao, WANG Weiye, LI Zechen, MA Tianyu. SAR Image Generation Method via PCGAN for Ship Detection[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20210630

面向舰船目标检测的SAR图像数据PCGAN生成方法

doi: 10.3969/j.issn.0258-2724.20210630
基金项目: 国家自然科学基金(62001437,61871335);中央高校基本业务费专项资金(2682020ZT35)
详细信息
    作者简介:

    潘磊(1986—),男,工程师,博士,研究方向为图像处理与模式识别,E-mail:mapan.lei@163.com

    通讯作者:

    郭宇诗(1998—),女,初级工程师,研究方向为图像处理与模式识别,E-mail:ysguo1634@163.com

  • 中图分类号: TP391.41;TP183

SAR Image Generation Method via PCGAN for Ship Detection

  • 摘要:

    针对现有合成孔径雷达(SAR)图像数据生成方法大多无法同时生成舰船图像及其检测标签的问题,面向SAR舰船图像生成及目标检测任务,构建基于位置信息的条件生成对抗网络(PCGAN). 首先,提出将舰船位置信息作为约束条件用于限制生成图像中舰船的位置,并将其作为舰船图像的检测标签;随后,引入Wasserstein距离稳定PCGAN的训练过程;最后,利用生成的SAR舰船图像及对应检测标签完成YOLOv3网络的端到端训练,实现舰船数据增强与目标检测的协同学习,进而获得更耦合目标检测实际应用的多样性数据. 在HRSID (high resolution SAR image dataset)数据集上的实验结果表明,PCGAN方法能生成清晰、鲁棒的SAR舰船数据,舰船检测准确度最高提升1.01%,验证了所提出方法的有效性.

     

  • 图 1  原始生成对抗网络结构

    Figure 1.  Original GAN structure

    图 2  SAR图像数据PCGAN生成方法整体框架

    Figure 2.  Framework of PCGAN based generation method for SAR images

    图 3  舰船位置信息图像形式转换过程

    Figure 3.  Conversion process of image form for ship position information

    图 4  PCGAN的生成器结构

    Figure 4.  Structure of PCGAN generator

    图 5  PCGAN的判别器结构

    Figure 5.  Structure of PCGAN discriminator

    图 6  HRSID数据集部分图像

    Figure 6.  Partial images of HRSID

    图 7  不同尺度/方向舰船目标的转换结果

    Figure 7.  Conversion results of ship targets with different scales or directions

    图 8  不同GAN网络的生成结果

    Figure 8.  Generation results of different GANs

    表  1  不同尺寸内舰船目标比重

    Table  1.   Proportion of ship targets in different sizes

    尺寸/像素 32 × 32 64 × 64 128 × 128
    比例/% 14.18 78.88 99.33
    下载: 导出CSV

    表  2  不同训练集下的实验结果

    Table  2.   Experimental results under different training sets %

    条件 I/张
    100 200 300
    增强前 91.01 92.91 93.11
    增强 1.0 倍 91.88 93.61 94.06
    增强 1.5 倍 91.79 93.73 94.12
    增强 2.0 倍 91.46 93.31 93.93
    增强 3.0 倍 91.48 93.33 94.00
    下载: 导出CSV

    表  3  不同GAN网络的生成图像质量

    Table  3.   Image quality of different GANs

    不同
    生成网络
    FID SWD
    舰船
    图像
    增强
    图像
    舰船
    图像
    增强
    图像
    GAN 221.9 126.3 3665 2519
    WGAN 66.06 96.92 181.7 1692
    条件 WGAN 157.1 102.8 448.3 1699
    条件 WGAN+方向 99.61 99.07 440.0 1620
    PCGAN 86.94 95.62 434.2 1513
    下载: 导出CSV

    表  4  消融实验结果

    Table  4.   Results of ablation experiments

    不同生成网络 准确度/%
    无生成网络 93.11
    条件WGAN 93.71
    条件WGAN+方向 93.88
    PCGAN 94.12
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-03
  • 修回日期:  2022-01-18
  • 网络出版日期:  2024-04-03

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