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基于生成对抗网络和线上难例挖掘的SAR图像舰船目标检测

李健伟 曲长文 彭书娟 江源

李健伟, 曲长文, 彭书娟, 江源. 基于生成对抗网络和线上难例挖掘的SAR图像舰船目标检测[J]. 电子与信息学报, 2019, 41(1): 143-149. doi: 10.11999/JEIT180050
引用本文: 李健伟, 曲长文, 彭书娟, 江源. 基于生成对抗网络和线上难例挖掘的SAR图像舰船目标检测[J]. 电子与信息学报, 2019, 41(1): 143-149. doi: 10.11999/JEIT180050
Jianwei LI, Changwen QU, Shujuan PENG, Yuan JIANG. Ship Detection in SAR images Based on Generative Adversarial Network and Online Hard Examples Mining[J]. Journal of Electronics & Information Technology, 2019, 41(1): 143-149. doi: 10.11999/JEIT180050
Citation: Jianwei LI, Changwen QU, Shujuan PENG, Yuan JIANG. Ship Detection in SAR images Based on Generative Adversarial Network and Online Hard Examples Mining[J]. Journal of Electronics & Information Technology, 2019, 41(1): 143-149. doi: 10.11999/JEIT180050

基于生成对抗网络和线上难例挖掘的SAR图像舰船目标检测

doi: 10.11999/JEIT180050
基金项目: 国家自然科学基金(61571454)
详细信息
    作者简介:

    李健伟:男,1989年生,博士生,研究方向为SAR图像处理、机器学习及深度学习

    曲长文:男,1964年生,教授,研究方向为雷达信号处理,信息对抗,信号与信息处理等

    彭书娟:女,1980年生,博士生,研究方向为SAR图像处理

    通讯作者:

    李健伟 lgm_jw@163.com

  • 中图分类号: TN957.51

Ship Detection in SAR images Based on Generative Adversarial Network and Online Hard Examples Mining

Funds: The National Natural Science Foundation of China (61571454)
  • 摘要:

    基于深度学习的SAR图像舰船目标检测算法对图像的数量和质量有很高的要求,而收集大体量的舰船SAR图像并制作相应的标签需要消耗大量的人力物力和财力。该文在现有SAR图像舰船目标检测数据集(SSDD)的基础上,针对目前检测算法对数据集利用不充分的问题,提出基于生成对抗网络(GAN)和线上难例挖掘(OHEM)的SAR图像舰船目标检测方法。利用空间变换网络在特征图上进行变换,生成不同尺寸和旋转角度的舰船样本的特征图,从而提高检测器对不同尺寸、旋转角度的舰船目标的适应性。利用OHEM在后向传播过程中发掘并充分利用难例样本,去掉检测算法中对样本正负比例的限制,提高对样本的利用率。通过在SSDD数据集上的实验证明以上两点改进对检测算法性能分别提升了1.3%和1.0%,二者结合提高了2.1%。以上两种方法不依赖于具体的检测算法,且只在训练时增加步骤,在测试时候不增加计算量,具有很强的通用性和实用性。

  • 图  1  Fast R-CNN原理示意图

    图  2  对抗空间变换网络示意图

    图  3  线上难样本挖掘流程(K=64)

    图  4  数据集SSDD中不同尺度和角度舰船检测结果

    表  1  4种方法检测性能

    方法mAP
    (%)
    训练时间
    (s)
    测试时间
    (s)
    标准的 Fast R-CNN68.00.6100.328
    标准的 Fast R-CNN+ GAN69.40.8230.326
    标准的 Fast R-CNN+OHEM69.11.1520.321
    标准的 Fast R-CNN+GAN
    +OHEM
    70.22.1090.330
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-01-15
  • 修回日期:  2018-09-26
  • 网络出版日期:  2018-10-22
  • 刊出日期:  2019-01-01

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