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利用自适应融合和混合锚检测器的遥感图像小目标检测算法

王坤 丁麒龙

王坤, 丁麒龙. 利用自适应融合和混合锚检测器的遥感图像小目标检测算法[J]. 电子与信息学报. doi: 10.11999/JEIT230966
引用本文: 王坤, 丁麒龙. 利用自适应融合和混合锚检测器的遥感图像小目标检测算法[J]. 电子与信息学报. doi: 10.11999/JEIT230966
WANG Kun, DING Qilong. Remote Sensing Images Small Object Detection Algorithm With Adaptive Fusion and Hybrid Anchor Detector[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230966
Citation: WANG Kun, DING Qilong. Remote Sensing Images Small Object Detection Algorithm With Adaptive Fusion and Hybrid Anchor Detector[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230966

利用自适应融合和混合锚检测器的遥感图像小目标检测算法

doi: 10.11999/JEIT230966
基金项目: 国家自然科学基金(62173331)
详细信息
    作者简介:

    王坤:女,副教授,研究方向为图像处理、故障诊断

    丁麒龙:男,硕士生,研究方向为遥感图像目标检测

    通讯作者:

    王坤 yogo_w@163.com

  • 中图分类号: TP751.1

Remote Sensing Images Small Object Detection Algorithm With Adaptive Fusion and Hybrid Anchor Detector

Funds: The National Natural Science Foundation of China (62173331)
  • 摘要: 针对遥感图像背景噪声多,小目标多且密集排列,以及目标尺度分布广导致的遥感图像小目标难以检测的问题,该文提出一种根据不同尺度的特征信息自适应融合的混合锚检测器AEM-YOLO。首先,提出了一种结合目标宽高信息以及尺度宽高比信息的二坐标系k-means聚类算法,生成与遥感图像数据集匹配度较高的锚框。其次,设计了自适应增强模块,用于解决不同尺度特征之间的直接融合导致的信息冲突,并引入更低特征层沿自底向上的路径传播小目标细节信息。通过混合解耦检测头的多任务学习以及引入尺度引导因子,可以有效提高对宽高比大的目标召回率。最后,在DIOR数据集上进行实验表明,相较于原始模型,AEM-YOLO的AP提高了7.8%,在小中大目标的检测中分别提高了5.4%,7.2%,8.6%。
  • 图  1  AEM-YOLO网络结构

    图  2  聚类结果图

    图  3  多尺度融合路径的类激活图

    图  4  自适应融合模块结构

    图  5  混合解耦检测头结构

    图  6  DIOR数据集中的目标在极坐标系下角度与尺度维度上的检测精度

    图  7  不同模块在DIOR数据集上的检测结果

    图  8  不同目标检测方法在DIOR数据集上的检测结果

    表  1  不同平衡系数对有锚检测分支精度的影响

    $ \beta $0.00.10.20.30.40.50.60.70.80.91.0
    mAP(%)81.581.682.082.282.382.182.282.682.482.582.3
    下载: 导出CSV

    表  2  训练参数设定

    参数名称迭代次数批处理大小动量权重衰减
    参数值200160.9370.0005
    下载: 导出CSV

    表  3  不同聚类算法在DIOR数据集实验结果对比(%)

    MethodAPAP50AP75APSAPMAPL
    预设锚框44.375.545.28.133.559.2
    k-means41.975.540.69.433.755.1
    二坐标系k-means44.776.245.811.034.858.5
    下载: 导出CSV

    表  4  不同多尺度融合模块在DIOR数据集实验结果对比(%)

    算法 AP AP50 AP75 APS APM APL
    FPN-PAN 47.4 77.6 49.3 11.4 36.2 62.3
    Bi-FPN 46.6 77.6 48.1 10.0 37.2 61.4
    ASFF 47.9 78.3 49.8 10.2 36.4 63.0
    AEM-F 47.6 78.0 49.8 10.9 36.0 62.9
    AEM-A 48.0 78.9 50.2 13.3 37.5 62.5
    AEM 49.5 79.0 52.2 12.9 37.3 65.0
    下载: 导出CSV

    表  5  不同检测头在DIOR数据集实验结果对比(%)

    算法 AP AP50 AP75 APS APM APL
    Coupled Head 44.7 76.2 45.8 11.0 34.8 58.5
    Decoupled Head 47.4 77.6 49.3 11.4 36.2 62.3
    ASFF Head 48.3 78.3 50.5 11.1 36.9 63.3
    Hybrid Head 50.5 79.4 53.2 12.6 39.1 66.2
    下载: 导出CSV

    表  6  在DIOR数据集上的消融实验结果(%)

    Pre-trainNeckHeadAPAP50AP75APSAPMAPLPara(M)FPS
    ×××44.375.545.28.133.559.264.061.2
    ××44.776.245.811.034.858.564.061.4
    ×49.579.052.212.937.365.074.052.4
    52.180.955.313.540.767.874.053.6
    下载: 导出CSV

    表  7  不同输入尺寸下在NWPU VHR-10数据集实验结果对比(%)

    算法输入尺寸APAP50AP75APSAPMAPL
    YOLOv4416×41640.583.635.618.438.851.7
    AEM-YOLO416×41645.188.240.629.943.557.5
    YOLOv4608×60844.488.835.222.540.045.5
    AEM-YOLO608×60851.991.749.830.747.054.0
    下载: 导出CSV

    表  8  不同目标检测方法在DIOR数据集实验结果对比

    EfficientDet RetinaNet ASFF YOLOv3 YOLOv4 YOLOv5 YOLOX YOLOv6 YOLOv7 YOLOv8 本文算法
    mAP(%) 65.3 69.0 79.9 74.5 76.9 77.8 62.9 79.3 78.9 82.3 82.6
    GFLOPs 21.0 152.3 82.4 65.7 64.0 86.6 54.2 60.8 80.0 109.1 95.8
    FPS 34.4 46.8 45.3 82.3 72.1 53.5 64.8 61.3 59.7 57.7 53.6
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-09-04
  • 修回日期:  2024-04-08
  • 网络出版日期:  2024-05-01

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