融合视觉显著性和局部熵的红外弱小目标检测

赵鹏鹏; 李庶中; 李迅; 罗军; 常凯

doi:10.37188/CO.2021-0170

融合视觉显著性和局部熵的红外弱小目标检测

doi: 10.37188/CO.2021-0170

赵鹏鹏^1,,
李庶中^1,,
李迅^1,,
罗军^1,,
常凯^2, ,

1.
海军研究院, 北京100036
2.
北方电子设备研究所, 北京 100036

基金项目: 国家自然科学基金青年科学基金项目（No. 62101589）

详细信息

作者简介:
赵鹏鹏（1988—），男，山东滕州人，博士，工程师，2016年于军械工程学院获得博士学位，主要从事光电系统总体设计、光学图像处理、目标识别等方面的研究。E-mail: zhaopengpeng5258@sina.com

常　凯（1987—），男，河北大城人，博士，2016年于军械工程学院获得博士学位，工程师，主要从事目标识别、系统工程方面的研究。E-mail: kerkai@163.com

中图分类号: TP391.4
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出版历程
- 收稿日期: 2021-09-13
- 修回日期: 2021-10-28
- 录用日期: 2022-01-06
- 网络出版日期: 2022-01-08
- 刊出日期: 2022-03-21

Infrared dim small target detection based on visual saliency and local entropy

ZHAO Peng-peng^1
,,
LI Shu-zhong^1
,,
LI Xun^1
,,
LUO Jun^1
,,
CHANG Kai^{2
, ,}

1.
Naval Research Institute, Beijing 100036, China
2.
Northern Institute of Electronic Equipment, Beijing 100036, China

Funds: Supported by the National Natural Science Foundation of China（No. 62101589）

More Information

Corresponding author: kerkai@163.com

摘要

摘要: 针对红外图像中弱小目标检测虚警率高、实时性差的问题，提出了一种基于视觉显著性和局部熵的红外弱小目标检测方法。该方法将红外弱小目标的检测问题由粗到精分步实现，首先利用融合局部熵的方法提取包含目标的感兴趣区域，对红外弱小目标实现粗定位。然后再利用改进的视觉显著性检测方法在感兴趣区域计算局部对比度，获得感兴趣区域的显著图。最后利用阈值法分割显著图像提取红外弱小目标，实现红外弱小目标的检测。通过与TOPHAT算法及LCM算法进行对比试验，验证了该方法在检测性能上优于TOPHAT算法以及LCM算法，虚警率分别下降了62.5%和33.3%；检测实时性方面，算法耗时为LCM的38.6%。该方法能够实现复杂背景下红外弱小目标的准确检测，在一定程度上解决了目标检测虚警率高、实时性差的问题。
- 视觉显著性 /
- 红外图像 /
- 弱小目标检测 /
- 局部熵
Abstract: To improve the high false-alarm rate and poor real-time capability in detecting infrared small dim targets, a novel algorithm based on visual saliency and local entropy is proposed in this paper. This method solves the problem from coarse to fine detecting of small targets. First, a local entropy method is used to obtain the region of interest. Then, an improved visual saliency method is used to calculate local contrast. Finally, a threshold segmentation method is used to extract dim infrared small targets. The method is verified using a contrast test with TOPHAT and LCM, and the results show that the performance of this method precedes the TOPHAT algorithm and LCM algorithm. The false alarm rate by this method decreases to 62.5% and 33.3% compared with the other two algorithms, and the time cost decrease to 38.6% of that of LCM. The method can achieve accurate detection of infrared dim and small targets in a complicated environment, solving the high false alarm rate and poor real-time capability issues to some extent.
- visual saliency /
- infrared images /
- dim small target detection /
- local entropy

HTML全文

图 1 视觉显著性和局部熵结合的弱小目标检测流程

Figure 1. Block diagram of target detection algorithm combining visual salient and local entropy

下载: 全尺寸图片幻灯片

图 2 本文算法红外弱小目标检测实验结果

Figure 2. IR small target detection results using the proposed algorithm

下载: 全尺寸图片幻灯片

图 3 3种目标检测算法对比实验结果

Figure 3. Contrast experiment results with three different target detection algorithms

下载: 全尺寸图片幻灯片

表 1 3种目标检测算法运行时间对比

Table 1. Computational cost comparison among three target detection algorithms

算法 TOPHAT算法 LCM算法本文算法

平均耗时/s 0.0307 1.4193 0.5481

下载: 导出CSV

参考文献(19)

[1]	黄乐弘, 曹立华, 李宁, 等. 深度学习的空间红外弱小目标状态感知方法[J]. 中国光学,2020,13(3):527-536. HUANG L H, CAO L H, LI N, et al. A state perception method for infrared dim and small targets with deep learning[J]. Chinese Optics, 2020, 13(3): 527-536. (in Chinese)
[2]	任向阳, 王杰, 马天磊, 等. 红外弱小目标检测技术综述[J]. 郑州大学学报(理学版),2020,52(2):1-21. REN X Y, WANG J, MA T L, et al. Review on infrared dim and small target detection technology[J]. Journal of Zhengzhou University (Natural Science Edition), 2020, 52(2): 1-21. (in Chinese)
[3]	马铭阳, 王德江, 孙翯, 等. 基于稳健主成分分析和多点恒虚警的红外弱小目标检测[J]. 光学学报,2019,39(8):0810001. doi: 10.3788/AOS201939.0810001 MA M Y, WANG D J, SUN H, et al. Infrared dim-small target detection based on robust principal component analysis and multi-point constant false alarm[J]. Acta Optica Sinica, 2019, 39(8): 0810001. (in Chinese) doi: 10.3788/AOS201939.0810001
[4]	周苑, 张健民, 林晓. 基于加权LoG算子的红外弱小目标检测方法研究[J]. 应用光学,2017,38(1):114-119. ZHOU Y, ZHANG J M, LIN X. Infrared small target detection using weighting LoG operator[J]. Journal of Applied Optics, 2017, 38(1): 114-119. (in Chinese)
[5]	魏然然, 詹伟达, 朱德鹏, 等. 改进多尺度的Retinex红外图像增强[J]. 液晶与显示,2021,36(3):465-474. doi: 10.37188/CJLCD.2020-0109 WEI R R, ZHAN W D, ZHU D P, et al. Improved multi-scale Retinex infrared image enhancement[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(3): 465-474. (in Chinese) doi: 10.37188/CJLCD.2020-0109
[6]	MAO X, DIAO W H. Criterion to evaluate the quality of infrared small target images[J]. Journal of Infrared,Millimeter,and Terahertz Waves, 2009, 30(1): 56-64. doi: 10.1007/s10762-008-9410-5
[7]	王刚, 陈永光, 杨锁昌, 等. 采用图像块对比特性的红外弱小目标检测[J]. 光学精密工程,2015,23(5):1424-1433. doi: 10.3788/OPE.20152305.1424 WANG G, CHEN Y G, YANG S CH, et al. Detection of infrared dim small target based on image patch contrast[J]. Optics and Precision Engineering, 2015, 23(5): 1424-1433. (in Chinese) doi: 10.3788/OPE.20152305.1424
[8]	何耀民, 何华锋, 徐永壮, 等. 基于改进小波变换的海上目标检测[J]. 系统工程与电子技术,2020,42(1):83-89. doi: 10.3969/j.issn.1001-506X.2020.01.12 HE Y M, HE H F, XU Y ZH, et al. Marine target detection based on improved wavelet transform[J]. Systems Engineering and Electronics, 2020, 42(1): 83-89. (in Chinese) doi: 10.3969/j.issn.1001-506X.2020.01.12
[9]	DIRAMI A, HAMMOUCHE K, DIAF M, et al. Fast multilevel thresholding for image segmentation through a multiphase level set method[J]. Signal Processing, 2013, 93(1): 139-153. doi: 10.1016/j.sigpro.2012.07.010
[10]	SUCCARY R, KALMANOVITCH H, SHURNIK Y, et al. Point target detection[J]. Proceedings of SPIE, 2003, 4820: 671-675. doi: 10.1117/12.453556
[11]	强勇, 焦李成, 保铮. 动态规划算法进行弱目标检测的机理研究[J]. 电子与信息学报,2003,25(6):721-727. QIANG Y, JIAO L CH, BAO ZH. Study on mechanism of dynamic programming algorithm for dim target detection[J]. Journal of Electronics and Information Technology, 2003, 25(6): 721-727. (in Chinese)
[12]	杨帆, 汪文英, 王茹琪. 基于粒子滤波TBD的高机动目标检测技术[J]. 中国电子科学研究院学报,2018,13(3):279-283. doi: 10.3969/j.issn.1673-5692.2018.03.008 YANG F, WANG W Y, WANG R Q. High maneuvering target detection technology based on particle filter TBD[J]. Journal of China Academy of Electronics and Information Technology, 2018, 13(3): 279-283. (in Chinese) doi: 10.3969/j.issn.1673-5692.2018.03.008
[13]	SERENCES J T, YANTIS S. Selective visual attention and perceptual coherence[J]. Trends in Cognitive Sciences, 2006, 10(1): 38-45. doi: 10.1016/j.tics.2005.11.008
[14]	ROLLS E T, DECO G. Attention in natural scenes: neurophysiological and computational bases[J]. Neural Networks, 2006, 19(9): 1383-1394. doi: 10.1016/j.neunet.2006.08.007
[15]	刘杨帆, 曹立华, 李宁, 等. 基于YOLOv4的空间红外弱目标检测[J]. 液晶与显示,2021,36(4):615-623. doi: 10.37188/CJLCD.2020-0227 LIU Y F, CAO L H, LI N, et al. Detection of space infrared weak target based on YOLOv4[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(4): 615-623. (in Chinese) doi: 10.37188/CJLCD.2020-0227
[16]	陈诗媛, 廖一鹏, 张进, 等. 结合NSST显著性检测及图割的泡沫红外图像分割[J]. 液晶与显示,2021,36(4):584-595. doi: 10.37188/CJLCD.2020-0234 CHEN SH Y, LIAO Y P, ZHANG J, et al. Foam infrared image segmentation combining NSST saliency detection and graph cuts[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(4): 584-595. (in Chinese) doi: 10.37188/CJLCD.2020-0234
[17]	ITTI L, KOCH C, NIEBUR E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259. doi: 10.1109/34.730558
[18]	CHEN C L 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, 2014, 52(1): 574-581. doi: 10.1109/TGRS.2013.2242477
[19]	黄果, 许黎, 陈庆利, 等. 非局部多尺度分数阶微分图像增强算法研究[J]. 电子与信息学报,2019,41(12):2972-2979. doi: 10.11999/JEIT190032 HUANG G, XU L, CHEN Q L, et al. Research on non-local multi-scale fractional differential image enhancement algorithm[J]. Journal of Electronics &Information Technology, 2019, 41(12): 2972-2979. (in Chinese) doi: 10.11999/JEIT190032