基于INet的雷达图像杂波抑制和目标检测方法

牟效乾 陈小龙 关键 周伟 刘宁波 董云龙

牟效乾, 陈小龙, 关键, 等. 基于INet的雷达图像杂波抑制和目标检测方法[J]. 雷达学报, 2020, 9(4): 640–653. doi: 10.12000/JR20090
引用本文: 牟效乾, 陈小龙, 关键, 等. 基于INet的雷达图像杂波抑制和目标检测方法[J]. 雷达学报, 2020, 9(4): 640–653. doi: 10.12000/JR20090
MOU Xiaoqian, CHEN Xiaolong, GUAN Jian, et al. Clutter suppression and marine target detection for radar images based on INet[J]. Journal of Radars, 2020, 9(4): 640–653. doi: 10.12000/JR20090
Citation: MOU Xiaoqian, CHEN Xiaolong, GUAN Jian, et al. Clutter suppression and marine target detection for radar images based on INet[J]. Journal of Radars, 2020, 9(4): 640–653. doi: 10.12000/JR20090

基于INet的雷达图像杂波抑制和目标检测方法

doi: 10.12000/JR20090
基金项目: 国家自然科学基金(U1933135, 61931021),山东省重点研发计划(2019GSF111004, 2019JZZY010415),基础加强计划技术领域基金(2102024)
详细信息
    作者简介:

    牟效乾(1995–),男,山东烟台人,硕士生。研究领域包括智能雷达信号处理、动目标检测等。E-mail: 1012226010@qq.com

    陈小龙(1985–),男,山东烟台人,博士,副教授。研究领域包括雷达信号处理、海杂波抑制、雷达智能探测等。入选中国科协“青年人才托举工程”,被评为中国电子学会优秀科技工作者,获中国电子学会优博,中国专利优秀奖,军队科技进步一等奖等。E-mail: cxlcxl1209@163.com

    关 键(1968–),男,辽宁锦州人,教授,博士生导师。主要研究方向包括雷达目标检测与跟踪、侦察图像处理和信息融合。获国家科技进步二等奖1项、军队科技进步一等奖2项,山东省技术发明一等奖1项;“百千万人才工程”国家级人选,入选教育部新世纪优秀人才支持计划。E-mail: guanjian_68@163.com

    周 伟(1980–),男,湖北黄石人,副教授,主要研究方向为多源信息融合、侦察图像处理、目标检测与识别。E-mail: yeaweam@gmail.com

    刘宁波(1983–),男,海军航空大学信息融合研究所副教授、博士,主要研究方向为雷达信号智能处理、海上目标探测技术。E-mail: lnb198300@163.com

    董云龙(1974–),男,天津宝坻人,教授,主要研究方向为多传感器信息融合。E-mail: china_dyl@sina.com

    通讯作者:

    陈小龙 cxlcxl1209@163.com

  • 责任主编:许述文 Corresponding Editor: XU Shuwen
  • 中图分类号: TN957.51

Clutter Suppression and Marine Target Detection for Radar Images Based on INet

Funds: The National Natural Science Foundation of China (U1933135, 61931021), The Key Research and Development Program of Shandong Province (2019GSF111004, 2019JZZY010415), The Fundamental Strengthening Technology Program (2102024))
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  • 摘要: 强海杂波与海面目标的复杂特性使得海面目标回波微弱,有效的海杂波抑制和稳健快速的目标检测是雷达对海上目标探测需考虑的重要因素。然而,现有的海面目标检测算法对于复杂环境下的目标检测性能有限,环境和目标特性适应性差。该文设计了一种杂波抑制和目标检测融合网络(INet),通过层归一化-传递和连接方法提取关键目标特征,采用注意力网络抑制杂波和增强目标,构建跨阶段局部残差网络保证检测网络的轻量化和准确性。基于导航雷达在多种观测条件下采集的回波数据,构建了海面目标雷达图像数据集;通过模型的预训练和平面位置显示器(PPI)图像的帧间积累对INet进行了优化,得到了Optimized INet(O-INet)模型。经过多种天气条件下实测数据测试和验证,并与YOLOv3, YOLOv4,双参数CFAR和二维CA-CFAR对比后证明,所提方法在提高检测概率、降低虚警率和复杂条件下的强泛化能力有显著优势。

     

  • 图  1  INet网络结构

    Figure  1.  The network structure of INet

    图  2  ARN网络结构

    Figure  2.  The network structure of ARN

    图  3  CSPRM结构

    Figure  3.  The network structure of CSPRM

    图  4  基于INet的目标检测算法流程图

    Figure  4.  Flowchart of target detection algorithm based on INet

    图  5  探测环境及雷达PPI界面

    Figure  5.  Detection environment and radar PPI interface

    图  6  Part Ⅰ 杂波抑制网络的部分特征图

    Figure  6.  Feature maps of the clutter suppression network in Part Ⅰ

    图  7  模型优化前后的对比(Data_03#)

    Figure  7.  Comparison before and after model optimization (Data_03#)

    图  8  低海况简单背景下的动目标(Data_01#)检测

    Figure  8.  Moving target detection under simple background of low sea state (Data_01#)

    图  9  低海况复杂背景下的动目标(Data_02#)检测

    Figure  9.  Moving target detection under complex background of low sea state (Data_02#)

    图  10  高海况下的多目标(Data_03#)检测

    Figure  10.  Multi-target detection under high sea conditions (Data_03#)

    图  11  大雪下的多目标(Data_04#)检测

    Figure  11.  Multi-target detection under heavy snow (Data_04#)

    图  12  中雨下的多目标(Data_05#)检测结果对比

    Figure  12.  Comparison of multi-target detection results under rain weather (Data_05#)

    图  13  低海况简单背景下的动目标(Data_01#)检测结果对比

    Figure  13.  Comparison of moving targets under simple background of low sea state (Data_01#)

    图  14  低海况复杂背景下的动目标(Data_02#)检测结果对比

    Figure  14.  Moving target detection under complex background of low sea state (Data_02#)

    图  15  高海况下的多目标(Data_03#)检测结果对比

    Figure  15.  Multi-target detection under high sea conditions (Data_03#)

    图  16  大雪下的多目标(Data_04#)检测结果对比

    Figure  16.  Multi-target detection under heavy snow (Data_04#)

    图  17  中雨下的动目标(Data_05#)检测结果对比

    Figure  17.  Comparison of multi-target detection results under rain weather (Data_05#)

    图  18  高海况下的多目标(Data_03#)检测结果

    Figure  18.  Multi-target detection results under high sea conditions (Data_03#)

    表  1  实时验证数据集参数

    Table  1.   Parameters of real-time verification dataset

    数据编号目标数量目标尺度目标速度海况背景PPI视频帧数
    Data_01#3中型船中速2级海杂波较弱150
    Data_02#2中、小型船低速2级地杂波较弱150
    Data_03#9中、小型船低速4级海杂波较强50
    Data_04#5中、小型船低速4级大雪50
    Data_05#2中、小型船低速4级中雨50
    下载: 导出CSV

    表  2  测试结果

    Table  2.   Test result

    算法RecallFA速度
    INet91.12%1.30%9.12 FPS
    下载: 导出CSV

    表  3  INet模型优化结果对比

    Table  3.   Comparison of optimization results

    算法简称Recall (%)FA (%)平均速度(FPS)
    INetINet91.121.129.12
    INet(预训练)\92.270.379.04
    INet +帧间积累\91.550.599.03
    INet (预训练)
    +帧间积累
    O-INet92.730.338.79
    下载: 导出CSV

    表  4  各类算法的实验结果对比

    Table  4.   Comparison of experimental results on different algorithms

    算法简称Recall(%)FA(%)平均速度(FPS)
    INet (ours)INet91.121.129.12
    INet (预训练)+帧间积累 (ours)O-INet92.730.338.79
    YOLOv3(预训练)[14]YOLOv383.234.059.25
    YOLOv4(预训练)[15]YOLOv489.042.7111.06
    下载: 导出CSV

    表  5  对测试集的检测结果(%)

    Table  5.   Test results about the test dataset (%)

    方法Pfa=10–4Pfa=10–3Pfa=10–2
    非相参积累+双参数CFAR16.9442.5257.71
    非相参积累+二维 CA-CFAR18.2537.7654.29
    O-INet80.9891.0493.65
    下载: 导出CSV
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  • 收稿日期:  2020-07-02
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