留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于局部对比度机制的红外弱小目标检测算法

韩金辉 董兴浩 蒋亚伟 李知铮 梁琨 张利红

韩金辉, 董兴浩, 蒋亚伟, 李知铮, 梁琨, 张利红. 基于局部对比度机制的红外弱小目标检测算法[J]. 红外技术, 2021, 43(4): 357-366.
引用本文: 韩金辉, 董兴浩, 蒋亚伟, 李知铮, 梁琨, 张利红. 基于局部对比度机制的红外弱小目标检测算法[J]. 红外技术, 2021, 43(4): 357-366.
HAN Jinhui, DONG Xinghao, JIANG Yawei, LI Zhizheng, LIANG Kun, ZHANG Lihong. Infrared Small Dim Target Detection Based on Local Contrast Mechanism[J]. Infrared Technology , 2021, 43(4): 357-366.
Citation: HAN Jinhui, DONG Xinghao, JIANG Yawei, LI Zhizheng, LIANG Kun, ZHANG Lihong. Infrared Small Dim Target Detection Based on Local Contrast Mechanism[J]. Infrared Technology , 2021, 43(4): 357-366.

基于局部对比度机制的红外弱小目标检测算法

基金项目: 

国家自然科学基金 61802455

国家自然科学基金 62003381

河南省科技厅科技发展计划项目 192102210089

河南省教育厅高等学校重点科研项目 18B510021

周口师范学院大学生创新创业训练计划项目 S202010478010

详细信息
    作者简介:

    韩金辉(1986-),男,河南周口人,讲师,博士,主要从事红外小目标检测、红外图像处理等研究。E-mail:hanjinhui@zknu.edu.cn

  • 中图分类号: TP391

Infrared Small Dim Target Detection Based on Local Contrast Mechanism

  • 摘要: 针对复杂背景和低信杂比条件下的红外弱小目标检测难题,提出了一种基于局部对比度机制的红外弱小目标检测方法。该方法提出了一个包含中心层、中间层和最外层的3层窗口,可以使用单尺度计算完成不同尺度弱小目标的检测。首先,对中心层引入匹配滤波思想,有针对性地增强真实目标;同时,提出最接近滤波原则,对最外层进行背景估计,以缓解目标靠近边缘时的检测难题;然后,在目标增强结果与背景估计结果之间进行比差联合的对比度计算,达到同时增强目标和抑制背景的目的;最后,通过自适应阈值分割,提取真实目标。实验结果表明,相比现有算法而言,该算法可更好地增强目标、抑制复杂背景,且原理简洁易实现,可有效减少运算量。
  • 图  1  本文算法框图

    Figure  1.  The flowchart of the proposed algorithm

    图  2  三层窗口

    Figure  2.  Three-layer window

    图  3  匹配滤波器模板

    Figure  3.  Template of the matched filter

    图  4  窗口处于不同位置的示意图,灰色区域代表普通背景,白色区域代表高亮背景

    Figure  4.  Different cases when the window at different positions, the grey area represents the normal background, the white area represents the bright background

    图  5  本文算法各个阶段处理后的图像(序列)

    Figure  5.  The processing result of each stage using the proposed algorithm(sequence)

    图  6  本文算法各个阶段处理后的图像(单帧数据库)

    Figure  6.  The processing result of each stage using the proposed algorithm(single-frame dataset)

    图  7  本文算法对于特殊情况检测失败的示例

    Figure  7.  Example of fail detection for the proposed algorithm under special condition

    图  8  不同序列的ROC曲线:(a)~(j):序列1~序列10

    Figure  8.  ROC curves of different sequences: (a)-(j): Seq. 1-Seq. 10

    表  1  10组红外序列的详细信息

    Table  1.   Details of the ten IR sequences

    Seq Frames Size Target size Target type
    1 200 320×240 7×5-4×3 Plane
    2 200 320×256 3×3 Plane
    3 200 320×256 3×5 Plane
    4 70 256×200 4×5-4×10 Plane
    5 200 256×320 2×3-3×4 Plane
    6 300 256×256 3×3-3×4 Unmanned vehicle
    7 300 256×320 2×2-3×3 Vehicle
    8 100 256×256 3×5 Vehicle
    9 100 256×256 5×5-5×7 Ship
    10 200 320×256 3×3 Simulation
    下载: 导出CSV

    表  2  单帧数据库中6幅图像的详细信息

    Table  2.   Details of the six frames in the single-frame dataset

    Signal image Size Target size Target type
    1 125×125 4×4 Plane
    2 122×122 3×4 Plane
    3 125×125 4×5 Plane
    4 122×122 3×4 Plane
    5 122×122 5×5 Plane
    6 122×122 3×4 Unmanned vehicle
    下载: 导出CSV

    表  3  各图像序列下(均以其中一帧为例)不同算法的SCRG值

    Table  3.   The SCRG values of different algorithms for different image sequences

    Seq Target DoG ILCM NLCM RLCM MPCM WLDM MDTDLMS TCF STLCF Proposed
    1 1 6.288 7.287 10.189 15.770 7.644 18.576 51.291 15.022 52.208 125.155
    2 1 9.388 17.002 30.240 17.670 9.558 45.192 101.235 24.942 66.480 268.112
    3 1 8.301 13.071 18.876 11.862 2.812 46.288 48.935 21.721 121.668 136.551
    4 1 1.913 6.096 5.619 2.556 2.926 7.844 12.683 1.113 18.236 30.208
    5 1 13.998 46.949 44.121 20.107 8.370 96.634 134.008 33.066 193.366 241.363
    6 1 1.145 4.464 5.573 3.839 1.812 6.612 12.713 2.007 14.492 32.199
    7 1 0.505 1.940 1.661 1.518 2.066 19.092 3.635 8.343 25.787 11.349
    8 1 1.528 2.583 4.453 2.579 2.941 4.266 8.711 1.014 3.535 37.353
    9 1 8.432 4.403 0.304 8.141 8.380 38.641 35.368 30.612 62.715 46.386
    2 6.086 14.072 7.662 7.071 6.465 34.149 44.157 3.869 3.957 44.898
    3 5.950 3.552 2.818 6.591 8.920 19.387 46.395 3.717 6.066 44.086
    4 5.331 3.422 0.065 6.591 6.974 18.155 37.250 4.195 7.411 43.760
    10 1 1.123 2.023 2.638 7.106 2.680 0.746 0.675 7.374 12.176 378.726
    下载: 导出CSV

    表  4  各图像序列下(均以其中一帧为例)不同算法的BSF值

    Table  4.   The BSF values of different algorithms for different image sequences

    Seq DoG ILCM NLCM RLCM MPCM WLDM MDTDLMS TCF STLCF Proposed
    1 2.124 33.948 1.412 6.139 0.576 681.672 3.423E3 14.658 4.279 5.360E3
    2 7.143 94.592 0.110 15.371 0.029 39.766 1.870E5 37.531 0.229 4.903E5
    3 3.231 49.586 0.036 11.120 0.020 15.127 1.347E5 30.609 0.120 3.708E5
    4 0.740 17.479 1.042 2.774 0.300 112.150 660.786 1.516 0.510 1.560E3
    5 5.706 46.369 0.022 15.135 0.019 22.857 3.894E5 31.337 0.145 6.984E5
    6 0.450 6.951 0.056 2.203 0.125 37.561 1.865E3 2.024 0.146 4.700E3
    7 0.481 9.174 0.034 2.198 0.136 16.595 4.242E3 8.431 0.030 1.318E4
    8 0.732 10.465 0.833 2.841 0.566 81.891 316.084 1.102 0.178 1.334E3
    9 2.160 29.866 0.658 5.264 0.742 536.948 2.326E3 32.869 4.776 3.008E3
    10 0.645 16.492 0.470 4.121 0.404 44.946 690.432 7.160 1.129 6.437E3
    下载: 导出CSV

    表  5  单帧图像库中不同算法的SCRG值

    Table  5.   The SCRG values of different algorithms for the single-frame image dataset

    Frame Target DoG ILCM NLCM RLCM MPCM WLDM MDTDLMS TCF STLCF Proposed
    1 1 3.667 6.671 3.125 2.814 1.366 25.676 0.607 - - 31.489
    2 1 1.106 1.881 0.542 1.750 0.765 9.651 0.608 - - 13.409
    3 1 4.277 13.183 9.048 5.356 2.257 13.942 2.445 - - 32.365
    4 1 1.659 2.775 4.963 1.513 1.047 4.593 0.182 - - 26.436
    5 1 2.038 1.431 1.395 2.064 3.500 9.955 0.181 - - 10.884
    6 1 0.469 2.195 3.988 2.522 1.281 4.358 0.995 - - 16.672
    下载: 导出CSV

    表  6  单帧图像库中不同算法的BSF值

    Table  6.   The BSF values of different algorithms for the single-frame image dataset

    Frame DoG ILCM NLCM RLCM MPCM WLDM MDTDLMS TCF STLCF Proposed
    1 1.630 5.904 0.065 2.742 0.235 58.382 172.100 - - 2.699E3
    2 0.332 2.677 0.034 1.360 0.260 14.742 93.599 - - 1.268E3
    3 1.127 11.319 0.142 2.856 0.148 32.569 357.666 - - 3.024E3
    4 0.558 9.072 0.318 1.528 0.292 23.423 90.231 - - 1.036E3
    5 0.635 4.898 0.082 1.513 0.358 29.906 60.236 - - 536.425
    6 0.199 3.619 0.025 1.308 0.118 4.992 268.685 - - 2.743E3
    下载: 导出CSV

    表  7  各种图像序列下不同算法的运行时间

    Table  7.   Running times of different algorithms for different image sequences seconds/frame

    Seq DoG ILCM NLCM RLCM MPCM WLDM MDTDLMS TCF STLCF Proposed
    1 0.232 0.083 0.076 1.739 2.205 4.192 19.110 0.361 0.980 0.902
    2 0.253 0.076 0.068 1.965 2.246 4.612 22.469 0.247 0.711 0.948
    3 0.269 0.088 0.072 2.166 2.500 4.560 22.232 0.230 0.670 0.949
    4 0.202 0.084 0.078 1.220 1.518 3.790 14.140 0.443 0.943 0.670
    5 0.262 0.101 0.072 2.023 2.217 4.708 20.826 0.225 0.868 0.972
    6 0.232 0.096 0.089 1.597 1.908 3.809 18.385 0.469 1.141 0.743
    7 0.285 0.088 0.079 2.117 2.430 4.981 21.576 0.234 0.673 0.912
    8 0.256 0.102 0.088 1.736 2.083 3.982 20.190 0.538 1.067 0.749
    9 0.209 0.073 0.063 1.566 1.780 3.977 18.346 0.208 0.647 0.762
    10 0.253 0.078 0.067 2.043 2.418 4.970 21.844 0.182 0.682 0.922
    下载: 导出CSV
  • [1] CUI Zheng, YANG Jingli, LI Junbao, et al. An infrared small target detection framework based on local contrast method[J]. Measurement, 2016, 91: 405-413. doi:  10.1016/j.measurement.2016.05.071
    [2] GAO Jinyan, LIN Zaiping, AN Wei. Infrared small target detection using a temporal variance and spatial patch contrast filter[J]. IEEE Access, 2019, 7: 32217-32226. doi:  10.1109/ACCESS.2019.2903808
    [3] BAI Xiangzhi, BI Yanguang. Derivative entropy-based contrast measure for infrared small-target detection [J]. IEEE Trans. Geosci. Remote Sens., 2018, 56(4): 2452-2466. doi:  10.1109/TGRS.2017.2781143
    [4] 潘胜达, 张素, 赵明, 等. 基于双层局部对比度的红外弱小目标检测方法[J]. 光子学报, 2020, 49(1): 184-192. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB202001021.htm

    PAN Shengda, ZHANG Su, ZHAO Ming, et al. Infrared small target detection based on double-layer local contrast measure[J]. Acta Photonica Sinica, 2020, 49(1): 184-192. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB202001021.htm
    [5] WANG Xin, LV Guogang, XU Lizhong. Infrared dim target detection based on visual attention[J]. Infrared Physics & Technology, 2012, 55(6): 513-521. http://www.sciencedirect.com/science/article/pii/S1350449512000801
    [6] CHEN C L P, LI Hong, WEI Yantao, et al. A local contrast method for small infrared target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 574-581. doi:  10.1109/TGRS.2013.2242477
    [7] HAN Jinhui, MA Yong, ZHOU Bo, 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
    [8] QIN Yao, LI Biao. Effective infrared small target detection utilizing a novel local contrast method[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12): 1890-1894. doi:  10.1109/LGRS.2016.2616416
    [9] 王晓阳, 彭真明, 张萍, 等. 局部对比度结合区域显著性红外弱小目标检测[J]. 强激光与粒子束, 2015, 27(9): 32-38. https://www.cnki.com.cn/Article/CJFDTOTAL-QJGY201509007.htm

    WANG Xiaoyang, PENG Zhenming, ZHANG Ping, et al. Infrared small dim target detection based on local contrast combined with region saliency [J]. High Power Laser and Particle Beams, 2015, 27(9): 32-38. https://www.cnki.com.cn/Article/CJFDTOTAL-QJGY201509007.htm
    [10] WEI Yantao, YOU Xinge, LI Hong. Multiscale patch-based contrast measure for small infrared target detection [J]. Pattern Recognition, 2016, 58: 216-226. doi:  10.1016/j.patcog.2016.04.002
    [11] HAN Jinhui, LIANG Kun, ZHOU Bo, et al. Infrared small target detection utilizing the multi-scale relative local contrast measure[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(4): 612-616. doi:  10.1109/LGRS.2018.2790909
    [12] DENG He, SUN Xianping, LIU Maili, et al. Entropy-based window selection for detecting dim and small infrared targets[J]. Pattern Recognition, 2017, 61: 66-77. doi:  10.1016/j.patcog.2016.07.036
    [13] KIM S, SUN S G, KIM K T. Highly efficient supersonic small infrared target detection using temporal contrast filter[J]. Electronics Letters, 2014, 50: 81-83. doi:  10.1049/el.2013.2109
    [14] DENG Lizhen, ZHANG Jieke, ZHU Hu. Infrared moving point target detection using a spatial-temporal filter[J]. Infrared Physics & Technology, 2018, 95: 122-127. http://www.sciencedirect.com/science/article/pii/S1350449518307916
    [15] MORADI S, MOALLEM P, SABAHI M F. Scale-space point function based framework to boost infrared target detection algorithms[J]. Infrared Physics & Technology, 2016, 77: 27-34. http://www.sciencedirect.com/science/article/pii/s1350449516300160
    [16] HAN Jinhui, LIU Sibang, QIN Gang, 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
    [17] 回丙伟, 宋志勇, 范红旗, 等. 地/空背景下红外图像弱小飞机目标检测跟踪数据集[J/OL]. 中国科学数据, 2020, 5(3): 286-297(DOI: 10.11922/csdata.2019.0074.zh).

    HUI Bingwei, SONG Zhiyong, FAN Hongqi, et al. A dataset for infrared detection and tracking of dim-small aircraft targets under ground/air background[J/OL]. China Scientific Data, 2020, 5(3): 286-297(DOI: 10.11922/csdata.2019.0074.zh).
    [18] OTCBVS WS Series Bench, Roland Miezianko, Terravic Research Infrared Database[DB/OL]. http://vcipl-okstate.org/pbvs/bench/index.html.
  • 加载中
图(8) / 表(7)
计量
  • 文章访问数:  293
  • HTML全文浏览量:  83
  • PDF下载量:  77
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-04-07
  • 修回日期:  2021-04-06
  • 刊出日期:  2021-04-20

目录

    /

    返回文章
    返回