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Infrared small target detection algorithm based on double neighborhood contrast measure

  • 1.

    School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China

  • 2.

    Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, Inner Mongolia University of Science and Technology, Baotou 014010, China

  • Corresponding author: ZHANG Baohua, zbh_wj2004@imust.cn ; 
  • Received Date: 2020-11-18
    Accepted Date: 2020-12-06
  • In order to solve the problem of missed detection easily caused in dense multi-target detection, an infrared small target detection algorithm based on double neighborhood contrast measure was proposed. First, the peak search algorithm was used to screen out the candidate targets; then the candidate targets were traversed through a single-scale three-layer double neighborhood window; finally the dual-neighbor contrast model was used to calculate the minimum gray contrast of the candidate target area, and the contrast and suppresses clutter were enhanced by the diagonal gradient. The results show that compared with the five comparison methods, the background suppression factor and contrast gain of this method are increased by 4.7 times and 1.8 times on average, respectively, which effectively suppresses clutter and enhances the target. This research can accurately detect multiple targets that are close to each other, which is helpful to improve the accuracy of multi-target detection in complex backgrounds.
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    CHEN Q X, WU W Ch, ASKAR H. Detection algorithm based on multi-scale spotted target modeling[J]. Laser Technology, 2020, 44(4): 520-524 (in Chinese).
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    GUO P Y, ZHANG B H. Infrared background suppression algorithm based on guided filtering and fuzzy algorithm[J]. Laser Technology, 2018, 42(6): 854-858(in Chinese).
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    WU Q, LIU H K, LIANG Sh N. Aerial infrared small target fast detection algorithm based on HVS[J]. Computer Engineering, 2019, 45(5): 210-215(in Chinese).
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    HAN J H, LIU S B, QIN G, et al. A local contrast method combined with adaptive background estimation for infrared small target detection[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(9): 1442-1446. doi: 10.1109/LGRS.2019.2898893
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    PAN Sh D, ZHANG S, ZHAO M, et al. Infrared small target detection based on double-layer local contrast measure[J]. Acta Photonica Sinica, 2020, 49(1): 0110003(in Chinese). doi: 10.3788/gzxb20204901.0110003
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    CHEN C P, LI H, WEI Y T, et al. A local contrast method for small infrared target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 52(1): 574-581.
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    HAN J H, LIANG K, ZHOU B, et al. Infrared small target detection utilizing the multiscale relative local contrast measure[J]. IEEE Geoscience & Remote Sensing Letters, 2018, 15(4): 612-616.
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    HAN J H, MA Y, ZHOU B, et al. A robust infrared small target detection algorithm based on human visual system[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(12): 2168-2172. doi: 10.1109/LGRS.2014.2323236
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    NASIRI M, CHEHRESA S. Infrared small target enhancement based on variance difference[J]. Infrared Physics & Technology, 2017, 82: 107-119.
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    WU L, MA Y, FAN F, et al. A double-neighborhood gradient method for infrared small target detection[J/OL]. (2020-06-29)[ 2020-11-18]. https://ieeexplore.ieee.org/document/9127878.
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    DU P, HAMDULLA A. Infrared small target detection using homogeneity-weighted local contrast measure[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 17(3): 514-518.
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    GUAN X W, PENG Z M, HUANG S Q, et al. Gaussian scale-space enhanced local contrast measure for small infrared target detection[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 17(2): 327-331.
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    ZHANG X Y, DING Q H, LUO H B, et al. Infrared dim target detection algorithm based on improved LCM[J]. Infrared and Laser Engineering, 2017, 46(7): 262-268(in Chinese).
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    QIN Y, LI B. Effective infrared small target detection utilizing a novel local contrast method[J]. IEEE Geoence and Remote Sensing Letters, 2016, 13(12): 1890-1894. doi: 10.1109/LGRS.2016.2616416
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    SHI Y F, WEI Y T, YAO H, et al. High-boost-based multiscale local contrast measure for infrared small target detection[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 15(1): 33-37.
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    HUANG S Q, PENG Z M, WANG Z R, et al. Infrared small target detection by density peaks searching and maximum-gray region growing[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(12): 1919-1923. doi: 10.1109/LGRS.2019.2912989
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    ZHANG K, YANG K, LI S Y, et al. A difference-based local contrast method for infrared small target detection under complex background[J]. IEEE Access, 2019, 7: 105503-105513. doi: 10.1109/ACCESS.2019.2932729
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Infrared small target detection algorithm based on double neighborhood contrast measure

    Corresponding author: ZHANG Baohua, zbh_wj2004@imust.cn; 
  • 1. School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
  • 2. Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, Inner Mongolia University of Science and Technology, Baotou 014010, China
  • Received Date: 2020-11-18
  • Accepted Date: 2020-12-06
  • Available Online: 2021-11-25

Abstract: In order to solve the problem of missed detection easily caused in dense multi-target detection, an infrared small target detection algorithm based on double neighborhood contrast measure was proposed. First, the peak search algorithm was used to screen out the candidate targets; then the candidate targets were traversed through a single-scale three-layer double neighborhood window; finally the dual-neighbor contrast model was used to calculate the minimum gray contrast of the candidate target area, and the contrast and suppresses clutter were enhanced by the diagonal gradient. The results show that compared with the five comparison methods, the background suppression factor and contrast gain of this method are increased by 4.7 times and 1.8 times on average, respectively, which effectively suppresses clutter and enhances the target. This research can accurately detect multiple targets that are close to each other, which is helpful to improve the accuracy of multi-target detection in complex backgrounds.

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0.   引言

  • 红外小目标检测是预警和精确制导等军事系统的核心技术[1-4]。由于远程成像，红外小目标通常在每帧中占据几十个像素，常淹没在复杂的背景中(如云边缘、海浪和高亮度噪声)，没有具体形状或纹理，而且信号杂波比较低。

    基于人类视觉系统(human vision system, HVS)的方法[5-15]具有良好的检测性能和实时性而得到广泛关注。CHEN等人[6]基于视觉对比机制提出一种局部对比测量方法(local contrast measurement, LCM)。HAN等人[8]发现LCM存在增强噪声点现象，提出了一种改进的局部对比测量方法(improved LCM, ILCM)。HAN等人[7]提出的相对局部对比测量方法(relative local contrast measurement, RLCM)，用多极值来抑制杂波干扰。基于多尺度的HVS方法在多目标密集分布的场景中，易将多目标识别为一个目标，导致漏检。其原因是采用多尺度技术通常以多尺度下的最大响应值作为最终输出，若窗口单元的尺寸大于源图像中的实际目标，靠近目标的背景区域也会被增强，检测到的目标会扩大，使源图像中小于9×9像素的目标在检测结果中显示为9×9像素，称之为“扩张效应”, 因而多目标的检测概率降低。

    为了解决“扩张效应”问题，提出了一种双邻域对比度算法。通过设计一种新的3层窗口，能在固定尺度下具有检测多尺度目标的能力，可以克服“扩张效应”，同时提高算法效率。在此基础上，利用基于双邻域对比度机制和对角梯度因子提高目标的显著性，更好地抑制背景干扰。

1.   相关工作

    1.1.   提取候选目标

  • 峰值搜索算法[16]假设：中心点被局部密度较低的近邻数据点包围，且任意中心点与比它密度更高的数据点间的距离都较远。

    对于任意像素点m, 需要计算m的局部密度ρm和最临近相关距离δm:

    式中，gm代表图像中像素点的灰度值。最临近相关距离δm则是通过计算点m与其它密度更高的点n之间的最小距离来测量：

    对于密度最高的点， ${\delta _m} = \mathop {\max }\limits_n \left( {{d_{mn}}} \right)$。

    式中，dmn是Manhattan距离，逐个像素计算联合特征因子γm，并按照降序排列把它们放到队列Q中，这里提取前12个像素点作为候选目标点; xm和ym代表点m的横坐标和纵坐标; xn和yn代表点n的横坐标和纵坐标。

    目标点在图像中具有局部差异性，即目标点的灰度值通常要高于局部相邻像素的灰度值。提取候选目标可以减小搜索目标的范围，减少算法计算时间。

    • 1.2.   3层双邻域窗口

  • 传统的局部对比度方法(LCM)、改进的LCM(ILCM)、相对LCM(RLCM)和基于差分的局部对比测量方法(difference local contrast measurement, DLCM)[17]采用多尺度技术检测1×2像素~9×9像素的小目标。对于红外图像中尺寸小于9×9像素的小目标，采用的多尺度技术将增强目标周围的背景面积，导致检测到的目标的大小扩大到9×9像素，即“扩张效应”，如图 1所示。“扩张效应”使两个目标重叠，不能精确地检测目标。因此，处理“扩张效应”的关键是找到一种非多尺度的方法，可以自适应地检测不同大小的目标。

    Figure 1.  Expansion effect of multi-scale methods

    为了在固定尺度下检测从2×1像素~9×9像素的小目标，设计了一个3层双邻域窗口，共包含25个单元，每个单元大小为3×3像素。如图 2所示，将整个窗口划分为3个区域，其中单元T是目标单元，单元T周围的8个单元Mi(i=1~8)代表中间单元，其余16个单元Bjk(j, k=1~4)表示背景单元。将候选目标置于3层双邻域窗口的中心时，小目标的灰度和梯度差异都会反映在3个区域中，用来检测不同大小的目标。同时3层双邻域窗口形状接近目标轮廓，可以准确逼近目标的真实分布。

    Figure 2.  Three-layer double-neighbor window structure

    • 1.3.   双邻域对比度计算

  • 小目标检测通过增强目标对比度，有效地突出小目标。根据3层双邻域窗口，充分利用3个区域之间的差异来测量对比度。中心单元和背景单元之间的最小灰度对比度d(T, Bjk)、中心单元和中间单元之间的梯度对比度d(T, Mi)分别表示为:

    式中，gT和gBjk分别代表中心单元和背景单元的灰度均值，GT和GMi分别代表中心单元和中间单元的梯度均值，其中i=1, 2, …, 8；j, k=1, 2, …, 4。为了进一步凸显目标和抑制杂波，采用中间区域梯度对比度对角相乘作为加权因子W，即：

    在局部区域内，真实目标强度通常高于局域背景强度，而虚假目标强度与局域背景强度相当，所以真实目标的d(T, Bjk)值较大；真实目标通常是中心对称，向四周辐射的圆点状，高亮杂波则普遍是不规则形状，所以虚假目标的对角梯度乘积通常为0。根据上述特征，候选目标位置的对比度信息为:

    对所有候选目标进行窗口遍历，真实目标区域对比度得到增强，背景杂波得到有效抑制，最终得到双邻域对比测量方法(double neighborhood contrast measurement, DNCM)目标映射图。

    • 1.4.   目标自适应提取

  • 根据DNCM的计算，可以得到图像的DNCM目标映射图。考虑到目标映射图具有不同层次的杂波背景，采用自适应阈值法对目标和背景进行分割。

    式中, μDNCM是DNCM目标映射图的均值，σDNCM是DNCM目标映射图的标准差，k是阈值系数，一般取30~50。经过阈值Tth分割得到真实目标。

2.   本文中的算法

  • 为解决人类视觉系统方法存在“扩张效应”，本文中提出一种基于双邻域对比度的红外小目标检测算法，整体算法结构如图 3所示。主要分为4个部分：峰值搜索算法提取候选目标、对候选目标构建3层双邻域窗口、双邻域对比度机制增强目标和抑制杂波、阈值分割提取真实目标。首先，为了减小目标搜索范围，利用峰值搜索算法构建ρ-δ (ρ代表密度，δ代表临近相关距离)特征空间图，将小目标检测看作异常点检测，根据联合特征因子筛选出异常点作为候选目标。其次，针对传统方法中存在缺陷的矩形窗口需要全图遍历的问题，构建3层双邻域窗口，对候选目标进行遍历, 并使窗口形状贴合目标轮廓，逼近目标的真实分布。然后，通过双邻域对比度机制计算候选目标的最小灰度对比度，结合梯度分布一致性，利用对角梯度因子进一步抑制杂波同时增强目标，计算得到DNCM目标映射图。最后通过自适应阈值分割得到真实目标。

    Figure 3.  Algorithm flowchart

    具体步骤如下：(1)对原始红外图像中每个像素点m计算局部密度ρm和最邻近相关距离δm，构建ρ-δ特征空间图，计算特征因子γ的值，并降序排列在队列中，提取前12个像素点作为候选目标；(2)对候选目标构建3层双邻域窗口，将3层双邻域窗口分为目标单元、中间单元和背景单元，共包含25个单元, 目标单元表示候选目标区域，中间单元代表目标过渡区域，背景单元是目标的局部背景区域；(3)利用双邻域对比度机制计算目标单元与背景单元的最小灰度对比度，目标单元与中间单元的对角梯度因子，通过哈达玛积运算得到DNCM目标映射图；(4)对DNCM目标映射图进行自适应阈值分割得到真实目标。

3.   实验结果分析

  • 本文中采用天空背景、云层背景和海天背景等5组单一目标或多目标的红外图像进行实验，并用本方法与顶帽变换[18]、方差差异方法(variance difference, VARD)[9]、LCM[6]、基于多尺度补丁的对比测量方法(multiscale patch-based contrast measurement，MPCM)[19]和RLCM[7]方法进行对比实验，图 4为实验结果，每张图像用矩形框显示真实目标区域。使用背景抑制因子(background suppression factor, BSF)、对比度增益(contrast gain, CG)和平均运行时间对所有算法进行评估，BSF可以全局评价算法的杂波抑制能力，其值与杂波抑制效果成正比；CG则可以评估目标的增强效果，其值与目标增强效果成正比。实验计算环境是3.40GHz Intel i7-3770 CPU处理器，8GB内存，所使用的测试软件是MATLAB 2018b。

    Figure 4.  Frame 1, 2, 3, 4, 5 five original image sequences and detection results under different methods

    很明显，图 4中采用多尺度技术的LCM和RLCM方法在5个图像序列中都出现了“扩张效应”，在frame 5检测结果中，邻近的两个目标发生重叠；顶帽变换和MPCM方法在背景抑制方面效果较差，检测结果有大量杂波；RLCM和VARD方法在frame 5中漏检目标；总体而言，本方法有效地解决了“扩张效应”并抑制了背景杂波。

    为了进一步量化分析，表 1中(加黑数字是最优结果)给出了5个图像序列经过6种不同检测算法处理后的BSF, CG和时间值。本方法的BSF和CG值在6种不同检测算法中均为最优，分别平均提高4.7倍和1.8倍，说明本方法背景抑制和目标增强能力高于基线水平；VARD和RLCM方法平均运行时间分别为最短和最长，本方法兼顾了检测精度和运算效率。

    methods top-hat VARD LCM MPCM RLCM proposed
    BSF frame 1 3.754 8.718 1.356 5.947 3.562 10.029
    frame 2 2.423 9.154 2.450 11.366 2.869 12.263
    frame 3 1.648 5.634 1.339 2.514 2.939 9.937
    frame 4 9.436 21.198 1.730 7.819 7.820 23.075
    frame 5 3.104 2.438 12.480 1.628 4.388 12.751
    CG frame 1 2.385 2.483 1.914 1.634 2.396 2.484
    frame 2 1.691 1.743 1.507 1.595 1.339 1.758
    frame 3 2.473 2.325 1.588 0.479 2.448 2.681
    frame 4 0.998 2.131 1.409 1.695 1.515 2.233
    frame 5 0.996 1.426 0.954 1.426 0.631 1.431
    time frame 1 0.570 0.067 0.116 0.103 1.049 0.336
    frame 2 0.380 0.066 0.135 0.118 2.650 0.815
    frame 3 0.430 0.069 0.150 0.126 3.553 1.023
    frame 4 0.470 0.082 0.167 0.153 4.553 1.200
    frame 5 0.732 0.102 0.131 0.076 3.358 0.873
4.   结论

  • 提出一种基于双邻域对比度的红外小目标检测算法，主要思想是设计一个3层窗口，在固定尺度下检测不同尺寸的小目标，解决了多尺度技术引起的“扩张效应”，降低计算复杂度。此外，利用双邻域对比度机制和对角梯度因子增强目标对比度，同时抑制背景干扰。实验结果表明，与其它方法相比，该方法具有较强的鲁棒性和实时性，特别是在检测相互接近的目标方面。后续将针对目标区域找寻新的定位方法，降低运行时间，构建鲁棒的特征空间用于检测真实目标。

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