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基于改进型YOLO算法的遥感图像舰船检测

王玺坤 姜宏旭 林珂玉

王玺坤, 姜宏旭, 林珂玉等 . 基于改进型YOLO算法的遥感图像舰船检测[J]. 北京航空航天大学学报, 2020, 46(6): 1184-1191. doi: 10.13700/j.bh.1001-5965.2019.0394
引用本文: 王玺坤, 姜宏旭, 林珂玉等 . 基于改进型YOLO算法的遥感图像舰船检测[J]. 北京航空航天大学学报, 2020, 46(6): 1184-1191. doi: 10.13700/j.bh.1001-5965.2019.0394
WANG Xikun, JIANG Hongxu, LIN Keyuet al. Remote sensing image ship detection based on modified YOLO algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(6): 1184-1191. doi: 10.13700/j.bh.1001-5965.2019.0394(in Chinese)
Citation: WANG Xikun, JIANG Hongxu, LIN Keyuet al. Remote sensing image ship detection based on modified YOLO algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(6): 1184-1191. doi: 10.13700/j.bh.1001-5965.2019.0394(in Chinese)

基于改进型YOLO算法的遥感图像舰船检测

doi: 10.13700/j.bh.1001-5965.2019.0394
基金项目: 

国家自然科学基金 61872017

航天科学技术基金 190109

详细信息
    作者简介:

    王玺坤  男, 硕士研究生。主要研究方向:嵌入式图像处理

    姜宏旭 男, 博士, 研究员, 博士生导师。主要研究方向:智能硬件、嵌入式图像处理

    林珂玉  女, 硕士研究生。主要研究方向:嵌入式图像处理

    通讯作者:

    姜宏旭, E-mail: jianghx@buaa.edu.cn

  • 中图分类号: TP391

Remote sensing image ship detection based on modified YOLO algorithm

Funds: 

National Natural Science Foundation of China 61872017

Aerospace Science and Technology Fund 190109

More Information
  • 摘要:

    目标检测算法在PASCAL VOC等数据集中取得了非常好的检测效果,但是在大尺度遥感图像中舰船目标的检测准确率却很低。因此,针对可见光遥感图像的特点,在YOLOv3-Tiny算法的基础上增加了特征映射模块,为预测层提供丰富的语义信息,同时在特征提取网络中引用残差网络,提高了检测准确率,从而有效提取舰船特征。实验结果表明:优化后的M-YOLO算法检测准确率为94.12%。相比于SSD和YOLOv3算法,M-YOLO算法的检测准确率分别提高了11.11%和9.44%。

     

  • 图 1  YOLOv3网络模型

    Figure 1.  YOLOv3 network architecture

    图 2  M-YOLO网络结构

    Figure 2.  M-YOLO network architecture

    图 3  特征映射模块

    Figure 3.  Feature mapping module

    图 4  残差模块

    Figure 4.  ResNet module

    图 5  ROC曲线

    Figure 5.  ROC curve

    图 6  舰船检测结果

    Figure 6.  Ship detection results

    表  1  PASCAL VOC数据集算法结果对比

    模型 mAP/%
    YOLOv3 YOLOv3-Tiny
    Person 70.89 57.96
    Bird 40.62 22.79
    Sheep 53.31 44.41
    Cow 53.39 43.68
    Dog 54.78 37.83
    Horse 76.13 59.96
    Motorbike 73.81 61.55
    均值 60.42 46.88
    模型 mAP/%
    YOLOv3 YOLOv3-Tiny
    Aeroplane 64.09 47.14
    Bicycle 71.05 61.44
    Boat 43.56 27.49
    Bus 68.16 58.10
    Car 75.87 65.39
    Cat 59.31 37.89
    Train 75.21 53.92
    均值 65.32 50.20
    模型 mAP/%
    YOLOv3 YOLOv3-Tiny
    Bottle 57.43 47.41
    Chair 29.32 13.45
    Diningtable 36.04 25.78
    Pottedplant 59.66 34.77
    sofa 27.60 20.53
    Tvmonitor 55.69 31.92
    均值 44.29 28.98
    下载: 导出CSV

    表  2  舰船数据集算法对比结果

    Table  2.   Ship dataset algorithm comparison results

    算法 AP/%
    YOLOv3 84.68
    YOLOv3-Tiny 87.23
    下载: 导出CSV

    表  3  舰船检测数据集

    Table  3.   Ship detection dataset

    参数 数值
    总数据集 60078
    训练集 18023
    测试集 24032
    训练验证集 360046
    验证集 180023
    下载: 导出CSV

    表  4  实验环境

    Table  4.   Lab environment

    参数 配置
    CPU Intel®xeon(R) CPU E5-2620 2.10GHz×12
    GPU GeForce GTX TITAN Xp
    系统 Ubuntu 16.04 LTS
    语言 Python 2.7
    加速环境 CUDA9.0,cuDNN7.0
    训练框架 Darknet
    下载: 导出CSV

    表  5  实验对比结果

    Table  5.   Experimental comparison results

    算法 检测准确率/% 帧率/(帧·s-1)
    SSD 83.01 41
    YOLOv3 84.68 43.4
    YOLOv3-Tiny 87.23 55.6
    M-YOLO 94.12 54.1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-07-19
  • 录用日期:  2019-10-18
  • 刊出日期:  2020-06-20

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