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RBNSM:一种复杂背景下红外弱小目标检测新方法

蔺素珍 张海松 禄晓飞 李大威 李毅

蔺素珍, 张海松, 禄晓飞, 李大威, 李毅. RBNSM:一种复杂背景下红外弱小目标检测新方法[J]. 红外技术, 2022, 44(7): 667-675.
引用本文: 蔺素珍, 张海松, 禄晓飞, 李大威, 李毅. RBNSM:一种复杂背景下红外弱小目标检测新方法[J]. 红外技术, 2022, 44(7): 667-675.
LIN Suzhen, ZHANG Haisong, LU Xiaofei, LI Dawei, LI Yi. RBNSM: a New Method for Infrared Dim and Small Target Detection in Complex Backgrounds[J]. Infrared Technology , 2022, 44(7): 667-675.
Citation: LIN Suzhen, ZHANG Haisong, LU Xiaofei, LI Dawei, LI Yi. RBNSM: a New Method for Infrared Dim and Small Target Detection in Complex Backgrounds[J]. Infrared Technology , 2022, 44(7): 667-675.

RBNSM:一种复杂背景下红外弱小目标检测新方法

基金项目: 

山西省自然科学基金项目 201901D111151

中北大学第十七届研究生科技立项项目 20201737

详细信息
    作者简介:

    蔺素珍(1966-),女,教授,博士,硕士生导师,主要从事图像处理、红外小目标检测和多波段图像融合领域研究。E-mail:lsz@nuc.edu.cn

  • 中图分类号: TP751.1

RBNSM: a New Method for Infrared Dim and Small Target Detection in Complex Backgrounds

  • 摘要: 弱小目标检测是红外探测与跟踪任务中的经典难题。针对复杂背景下红外弱小目标普遍存在检测率低、虚警率高的问题,提出一种基于区域双邻域显著图(Regional Bi-Neighborhood Saliency Map,RBNSM)的复杂背景红外弱小目标检测新方法。利用弱小目标的局部先验特性定义滑动窗口并划分为多个单元,计算中心单元前若干个最大灰度的均值来凸显弱目标;分别构建中心单元的相接邻域和相隔邻域并计算各自的灰度均值,进而,从不同方向上提取两邻域显著图并点乘二者以进一步抑制杂波背景、增强弱小目标;最后,通过自适应提取准确检测目标。多种典型红外复杂背景图像和SIRST数据集检测结果表明:与7种代表性方法相比,RBNSM在复杂背景下具有更好的检测性能与杂波抑制能力。
  • 图  1  RBNSM总体框架

    Figure  1.  General framework of RBNSM

    图  2  目标单元与背景单元分布

    Figure  2.  Distribution of target and background cells

    图  3  五种不同场景的复杂图像三维图及本方法显著性图

    Figure  3.  Three-dimensional maps of complex images of five different scenes and the saliency map of this method

    图  4  不同红外场景下7种算法的ROC曲线

    Figure  4.  ROC curves of 7 algorithms in different infrared scenarios

    图  5  不同尺度目标的红外图像原图、标签图及本方法结果

    Figure  5.  Original Infrared image, label image and result image of targets with different scales

    图  6  多目标的红外图像原图、标签图及本方法检测结果

    Figure  6.  The original infrared image, label image and detection result of the multi-target

    表  1  本方法在SIRST数据集上不同K值与λ值的nIoU值

    Table  1.   nIoU values for different K and λ values of this method on the SIRST dataset

    K 1 2 3 4 5 6
    nIoU 0.5472 0.5558 0.5467 0.5366 0.5131 0.4888
    K 7 8 9
    nIoU 0.4517 0.4186 0.3862
    λ 0 1 2 3 4 5
    nIoU 0.4846 0.5445 0.5558 0.5533 0.5499 0.5466
    下载: 导出CSV

    表  2  测试图像基本特征

    Table  2.   Basic features of the test image

    Image Size Target Size SCR Scene Description
    a 126×127 4×3 1.7862 Building edge and heavy noise
    b 128×128 3×3 1.4275 White patches and heavy noise
    c 255×320 3×2 1.1152 Faint targets drown in background
    d 305×405 2×3 0.8040 Strong cloud interference
    e 256×256 3×3 1.1950 Strong building edge interference
    下载: 导出CSV

    表  3  5种不同场景红外图像的SCRG,BSF和时间消耗

    Table  3.   SCRG, BSF and time consumption for 5 different scenes of IR images

    Image Index AAGD[26] PSTNN[17] LEF[24] TLLCM[25] HBMLCM[22] RLCM[23] RBNSM
    a SCRG
    BSF
    Time/s
    3.4482
    2.5841
    0.0103
    7.2142
    3.2782
    0.1454
    21.0433
    6.1053
    2.8768
    10.7899
    3.0621
    1.5073
    6.0033
    2.9321
    0.0383
    8.7069
    1.0584
    2.3580
    55.8439
    10.7154
    0.1193
    b SCRG
    BSF
    Time/s
    3.4781
    6.3289
    0.0133
    3.4120
    12.5161
    0.1023
    5.5669
    4.4671
    2.0580
    7.7367
    4.3538
    1.4525
    3.3712
    2.7164
    0.0110
    0.8943
    1.7737
    2.1472
    65.5746
    18.5168
    0.1476

    c
    SCRG
    BSF
    Time/s
    0.0524
    10.4722
    0.0387
    0.0137
    26.0004
    0.5546
    29.4364
    11.5055
    10.2756
    20.0115
    11.5709
    6.0454
    0.3882
    8.9662
    0.0434
    1.2931
    4.1872
    10.7094
    126.9818
    38.0842
    0.5666

    d
    SCRG
    BSF
    Tim/s
    15.0705
    1.1310
    0.0537
    119.5938
    32.2470
    1.1583
    66.1633
    23.6170
    18.5141
    36.5679
    11.6517
    8.5484
    78.5632
    19.0847
    0.0651
    11.8546
    5.7182
    15.3649
    180.5421
    183.3723
    1.6434
    e SCRG
    BSF
    Time/s
    16.4635
    4.6741
    0.0276
    28.2547
    11.6653
    0.4174
    53.0661
    18.7079
    8.6336
    69.5144
    26.1979
    5.4414
    31.0343
    10.7358
    0.0257
    25.8079
    4.6734
    8.5788
    217.6813
    50.7863
    0.4615
    下载: 导出CSV

    表  4  每种方法在SIRST数据集上的平均指标

    Table  4.   Average metrics for each method on the SIRST dataset

    AAGD PSTNN LEF TLLCM HBMLCM RLCM ACM[11] RBNSM
    SCRG 167.795 543.545 120.466 200.517 174.422 97.610 - 964.319
    BSF 34.174 73.079 22.717 46.599 61.351 9.235 - 280.170
    nIoU 0.2871 0.5987 0.2282 0.1943 0.2639 0.1418 0.4154 0.5558
    Time(CPU/s) 0.012 0.138 8.557 4.032 0.026 6.515 1.137 0.438
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-10-10
  • 修回日期:  2021-12-08
  • 刊出日期:  2022-07-20

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