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难点注意力感知红外小目标检测网络

王伯霄 宋延嵩 董小娜

王伯霄, 宋延嵩, 董小娜. 难点注意力感知红外小目标检测网络[J]. 中国光学(中英文), 2024, 17(3): 538-547. doi: 10.37188/CO.2023-0178
引用本文: 王伯霄, 宋延嵩, 董小娜. 难点注意力感知红外小目标检测网络[J]. 中国光学(中英文), 2024, 17(3): 538-547. doi: 10.37188/CO.2023-0178
WANG Bo-xiao, SONG Yan-song, DONG Xiao-na. Indistinguishable points attention-aware network for infrared small object detection[J]. Chinese Optics, 2024, 17(3): 538-547. doi: 10.37188/CO.2023-0178
Citation: WANG Bo-xiao, SONG Yan-song, DONG Xiao-na. Indistinguishable points attention-aware network for infrared small object detection[J]. Chinese Optics, 2024, 17(3): 538-547. doi: 10.37188/CO.2023-0178

难点注意力感知红外小目标检测网络

doi: 10.37188/CO.2023-0178
基金项目: 国家重点研发计划(No. 2022YFB3902505);国家自然科学基金重点项目(No. U2141231);国家自然科学基金(No. 62305032)
详细信息
    作者简介:

    宋延嵩(1983—),男,吉林长春人,长春理工大学空间光电技术研究所研究员,博士生导师,主要从事空间激光通信等方面的研究。E-mail:songyansong2006@126.com

  • 中图分类号: V19;E928.9;TN911.73

Indistinguishable points attention-aware network for infrared small object detection

Funds: Supported by National Key Research and Development Program (No. 2022YFB3902505); Key Project of National Natural Science Foundation of China (No. U2141231); National Natural Science Foundation of China (No. 62305032)
More Information
  • 摘要:

    随着飞行器机动性能的提升,多帧红外小目标检测方法不足以满足检测要求。近年来,基于深度学习的单帧红外小目标检测方法取得了巨大成功。然而,红外小目标通常缺少形状特征,而且边界与背景模糊不清,给准确分割带来了一定的挑战。针对上述问题,本文提出难点注意力感知红外小目标检测网络。通过基于点的区域建议模块获取目标潜在区域,同时滤除多余背景。为实现高质量分割、细化掩码边界模块、判断粗掩码中无序、非局部难以分辨点,融合这些难点的多尺度特征,进行逐像素注意力建模。最后,由点检测头对难点注意力感知特征重新预测,生成精细分割掩码。在公开数据集NUDT-SIRST和IRDST上进行测试,平均精度均值mAP达到87.4和63.4,F值达到0.8935和0.7056。本文提出的难点注意力感知红外小目标检测网络可在多检测场景、多目标形态下实现准确分割,抑制误报信息,同时控制计算开销。

     

  • 图 1  难点注意力感知网络结构图

    Figure 1.  Indistinguishable points attention-aware network architecture

    图 2  (a)中心点补偿边界;(b)基于点的区域建议模块

    Figure 2.  (a) Centre point offset to boundary; (b) point-based region proposal module

    图 3  各算法在(a) NUDT-SIRST 数据集和(b) IRDST 数据集 ROC 曲线

    Figure 3.  ROC curves for methods on (a) the NUDT-SIRST dataset and (b) the IRDST dataset

    图 4  各方法不同场景检测结果,相同目标放大显示于同颜色框内

    Figure 4.  Different scene detection results of different methods, with the same target zoomed in the same color box

    图 5  各方法检测结果 3D 可视化

    Figure 5.  3D visualization of detection results for each method

    图 6  区域建议可视化结果。(a)原图;(b)真值;(c)中心点热图及区域建议边界

    Figure 6.  Visualization of regional proposals. (a) Original maps; (b) groundtruths; (c) centroid heatmap and regional proposal boundaries

    图 7  难点分布可视化。(a)无人机目标;(b)点目标;(c)飞机目标

    Figure 7.  Visualization of indistinguishable points distribution. (a) UAV target; (b) point target; (c) aircraft target

    表  1  传统算法超参数设置

    Table  1.   Hyperparameter settings of traditional algorithms

    传统算法超参数设置
    Top-hatNhood=ones(5)
    LEFh=0.2,α=0.5, P=9
    AADCDD内窗口尺寸={3, 5, 7, 9},外窗口尺寸=19
    TLLCM窗口尺寸={3, 5, 7, 9},k=9
    下载: 导出CSV

    表  2  各方法在NUDT-SIRST及IRDST数据集定量结果对比

    Table  2.   Comparison of quantitative results of different methods on NUDT-SIRST and IRDST datasets

    检测算法 NUDT-SIRST IRDST
    mAP F值(Pre,Rec) mAP F值(Pre,Rec)
    Top-hat 1.5 0.3599(0.2850,0.4884) 0.7 0.0088(0.0045,0.4107)
    LEF 6.4 0.1151(0.0748,0.2498) 2.5 0.1219(0.0686,0.5470)
    AADCDD 1.6 0.1490(0.3838,0.0924) 1.4 0.0705(0.0521,0.1090)
    TLLCM 16.5 0.0724(0.0479,0.1476) 6.1 0.1881(0.1254,0.3759)
    ALCNet 69.3 0.7595(0.7035,0.8251) 46.5 0.5929(0.5461,0.6486)
    DNANet 86.9 0.8645(0.9070,0.8259) 62.1 0.6697(0.712 4,0.6319)
    RDIAN 82.4 0.890 00.899 00.881 1 60.0 0.7102(0.7092,0.7113
    本文方法 87.4 0.8935(0.8923,0.8948 63.4 0.7056(0.7183,0.6935)
    下载: 导出CSV

    表  3  深度学习方法单张图片平均推理时间

    Table  3.   Average inference times of a single image for deep learning methods (s)

    检测算法 NUDT-SIRST IRDST
    ALCNet 0.104 0.166
    DNANet 0.089 0.259
    RDIAN 0.065 0.114
    本文算法 0.099 0.121
    下载: 导出CSV

    表  4  不同区域建议模块对比表

    Table  4.   Comparison of different region proposal modules

    建议数量 基于点的区域建议 RPN
    mAP F值 mAP F值
    1000 87.9 0.8927 86.2 0.8425
    256 87.5 0.8962 85.8 0.8412
    128 87.4 0.8935 85.2 0.8406
    64 86.0 0.8901 84.5 0.8397
    下载: 导出CSV

    表  5  不同选点策略检测结果

    Table  5.   Detection results of different point selection strategies

    选点策略mAP
    均匀选点86.7
    k=1,γ=0.0086.9
    k=3,γ=0.7587.4
    k=10,γ=1.0085.8
    下载: 导出CSV

    表  6  难点不同特征融合结果

    Table  6.   Fusion results of different features at indistinguishable points

    细粒度特征粗糙掩码位置嵌入mAP
    85.5
    85.8
    87.4
    下载: 导出CSV

    表  7  不同细化方案检测结果

    Table  7.   Results of different refinement strategies

    细化方案mAP
    CNN(16×16)85.5
    MLP(16×16)86.2
    细化掩码边界模块(S=3)87.4
    细化掩码边界模块(S=6)87.6
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-10-11
  • 修回日期:  2023-10-30
  • 录用日期:  2023-12-05
  • 网络出版日期:  2024-01-16

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