基于深度分离卷积神经网络的高速高精度SAR舰船检测

张晓玲 张天文 师君 韦顺军

张晓玲, 张天文, 师君, 等. 基于深度分离卷积神经网络的高速高精度SAR舰船检测[J]. 雷达学报, 2019, 8(6): 841–851. doi: 10.12000/JR19111
引用本文: 张晓玲, 张天文, 师君, 等. 基于深度分离卷积神经网络的高速高精度SAR舰船检测[J]. 雷达学报, 2019, 8(6): 841–851. doi: 10.12000/JR19111
ZHANG Xiaoling, ZHANG Tianwen, SHI Jun, et al. High-speed and High-accurate SAR ship detection based on a depthwise separable convolution neural network[J]. Journal of Radars, 2019, 8(6): 841–851. doi: 10.12000/JR19111
Citation: ZHANG Xiaoling, ZHANG Tianwen, SHI Jun, et al. High-speed and High-accurate SAR ship detection based on a depthwise separable convolution neural network[J]. Journal of Radars, 2019, 8(6): 841–851. doi: 10.12000/JR19111

基于深度分离卷积神经网络的高速高精度SAR舰船检测

doi: 10.12000/JR19111
基金项目: 国家自然科学基金(61571099, 61501098, 61671113),国家重点研发计划(2017YFB0502700)
详细信息
    作者简介:

    张晓玲(1964–),女,四川人,获电子科技大学工学博士学位,目前为电子科技大学教授/博导,主要从事SAR成像技术、雷达探测技术研究、3维SAR成像的目标散射特性(RCS)反演。E-mail: xlzhang@uestc.edu.cn

    张天文(1994–),男,江苏人,现于电子科技大学信息与通信工程学院攻读博士学位,主要研究领域为SAR成像技术、遥感图像处理与智能识别解译。E-mail: twzhang@std.usetc.edn.cn

    师 君(1979–),男,河南人,获电子科技大学工学博士学位,目前为电子科技大学副教授,主要从事SAR数据处理方面研究。E-mail: shijun@uestc.edu.cn

    韦顺军(1983–),男,广西人,获电子科技大学工学博士学位,目前为电子科技大学副教授,主要从事SAR成像技术、干涉SAR技术研究。E-mail: weishunjun@uestc.edu.cn

    通讯作者:

    张晓玲 xlzhang@uestc.edu.cn

  • 中图分类号: TN957.52

High-speed and High-accurate SAR Ship Detection Based on a Depthwise Separable Convolution Neural Network

Funds: The National Natural Science Foundation of China (61571099, 61501098, 61671113), The National Key R&D Program of China (2017YFB0502700)
More Information
  • 摘要: 随着人工智能的兴起,利用深度学习技术实现SAR舰船检测,能够有效避免传统的复杂特征设计,并且检测精度获得了极大的改善。然而,现如今大多数检测模型往往以牺牲检测速度为代价来提高检测精度,限制了一些SAR实时性应用,如紧急军事部署、迅速海难救援、实时海洋环境监测等。为了解决这个问题,该文提出一种基于深度分离卷积神经网络(DS-CNN)的高速高精度SAR舰船检测方法SARShipNet-20,该方法取代传统卷积神经网络(T-CNN),并结合通道注意力机制(CA)和空间注意力机制(SA),能够同时实现高速和高精度的SAR舰船检测。该方法在实时性SAR应用领域具有一定的现实意义,并且其轻量级的模型有助于未来的FPGA或DSP的硬件移植。

     

  • 图  1  传统卷积神经网络和深度分离卷积神经网络示意图

    Figure  1.  Diagrammatic sketch of T-CNN and DS-CNN

    图  2  网络结构示意图 (SARShipNet-20)

    Figure  2.  Network structure (SARShipNet-20)

    图  3  卷积层内部操作流程

    Figure  3.  Internal operation flow in convolution layers

    图  4  通道注意力机制

    Figure  4.  Channel Attention (CA)

    图  5  空间注意力机制

    Figure  5.  Spatial Attention (SA)

    图  6  SARShipNet-20的SAR舰船检测结果

    Figure  6.  SAR ship detection results of SARShipNet-20

    图  7  SARShipNet-20性能评价曲线

    Figure  7.  Performance evaluation curve of SARShipNet-20

    表  1  SARShipNet-20的SAR舰船检测结果评价指标

    Table  1.   Evaluation index of SAR ship detection results of SARShipNet-20

    类型GTTPFNFPPd (%)Pm (%)Pf (%)Recall (%)Precision (%)mAP (%)Time (ms)
    T-CNN1841804897.832.174.2697.8395.7496.8810.14
    DS-CNN18417592395.114.8911.6295.1188.3893.644.54
    DS-CNN + CA18417952997.282.7213.9497.2889.0695.785.68
    DS-CNN + SA18417861196.743.265.8296.7494.1895.646.67
    DS-CNN + CA + SA1841804897.832.174.2697.8395.7496.938.72
    下载: 导出CSV

    表  2  不同方法的检测性能对比

    Table  2.   Comparison of detection performance of different methods

    方法Pd (%)Pm (%)Pf (%)Recall (%)Precision (%)mAP (%)Time (ms)
    Faster R-CNN[16]85.1614.8418.8585.1681.1582.66327.48
    RetinaNet[34]96.703.306.8896.7093.1295.68314.43
    R-FCN[35]95.654.357.3795.6592.6395.15178.16
    SSD[18]94.515.4914.8594.5185.1592.6748.86
    YOLOv3[20]96.703.306.3896.7093.6295.3422.30
    YOLOv1[28]84.0715.9315.4784.0784.5381.2421.95
    YOLOv2[29]92.867.1415.0892.8684.9290.0919.01
    YOLOv3-tiny[20]70.3329.1222.2970.3377.5864.6410.25
    YOLOv2-tiny[29]47.8052.2026.2747.8073.7344.409.43
    SARShipNet-20(本文方法)97.832.174.2697.8395.7496.938.72
    下载: 导出CSV

    表  3  不同方法的模型对比

    Table  3.   Model comparison of different methods

    方法网络参数的数量浮点运算量(FLOPs)模型大小 (MB)
    Faster R-CNN272,746,867545,429,460752.75
    RetinaNet61,576,342307,592,895235.44
    R-FCN50,578,686101,385,166193.04
    SSD47,663,80695,040,404181.24
    YOLOv336,382,95772,545,184139.25
    YOLOv128,342,19546,981,897,900108.54
    YOLOv223,745,908118,685,13390.73
    YOLOv3-tiny15,770,51031,608,36060.22
    YOLOv2-tiny8,676,24486,692,28433.20
    SARShipNet-20(本文方法)5,867,73711,699,79223.17
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-12-16
  • 修回日期:  2019-12-23
  • 网络出版日期:  2019-12-01

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