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Received：11 November 2019，
Revised：2020-1-8，
Accepted：15 January 2020，
Published：16 September 2020
移动端阅览
红外探测技术具有不受环境等因素干扰的优势，在红外制导、预警等军事领域的应用日益广泛。随着对红外弱小目标检测技术的研究越来越深入，相应的检测方法越来越多样。本文通过对红外弱小目标图像中目标与背景的特性以及红外弱小目标检测技术难点问题进行分析，根据当前是否利用帧间相关信息，分别从基于单帧红外图像和基于红外序列两个角度，选取了相应的红外弱小目标算法进行对比，对其中典型算法的原理、流程以及特点等进行了详细综述，并对每类检测算法的性能进行了比较。针对红外弱小目标图像信噪比低的特点，对红外弱小目标检测算法的难点问题进行分析，给出了目前各种算法的解决方法和不足，探讨红外弱小目标检测算法的发展方向，即研究计算量小、性能优、鲁棒性强、实时性好和便于硬件实现的算法。
Infrared acquisition technologies are not easily disturbed by environmental factors and have strong penetrability. In addition
the effect of infrared acquisition is mainly determined by the temperature of the object itself. Therefore
such technology has been widely used in the military field
such as in infrared guidance
infrared antimissile
and early warning systems. With the rapid development of computer vision and digital image processing technologies
infrared small-target detection has gradually become the focus and challenge of research
and the number of relevant methods and kinds of infrared small-target detection techniques are increasing. However
given the characteristics of small imaging area
long distance
lack of detailed features
weak shape features
and low signal-to-noise ratio
infrared dim- and small-target detection technology has always been a key technical problem in infrared guidance systems. In this study
two kinds of methods
which are based on single-frame images and infrared sequence and extensively used at present
are reviewed. This work serves as basis for follow-up research on the theory and development of small-target detection. The corresponding infrared small-target algorithm is selected for comparison on the basis of the analysis of the characteristics of the target and background in infrared small-target images and the difficulties of infrared small-target detection technology
in accordance with whether the interframe correlation information is used
and from the perspective of single-frame infrared image and infrared sequence. Single-frame based algorithms can be divided into three categories
including filtering methods
human vision system based methods low-rank sparse recovery base methods.The method based on filtering estimates the background of infrared images
using the frequency difference among the target
background and clutter to filter the background and clutter
to achieve the effect of background suppression. The method based on human vision systems mainly uses the visual perception characteristics of human eyes
that is
the appearance of small targets results in considerable changes of local texture rather than global texture. In recent years
the method based on low-rank sparse recovery has been widely used; it is also an algorithm with improved effect in single-frame image detection. This kind of algorithm maximizes the sparsity of small targets
the low rank of backgrounds
and the high frequency of clutter. Moreover
it uses optimization algorithms to solve the objective function and gradually improve the accuracy of detection in the process of iteration. However
this kind of infrared small-target detection method based on single-frame images requires a high signal-to-noise ratio and does not take advantage of the correlation between adjacent frames; thus
it is prone to false detection and demonstrates a relatively poor performance in real time. Therefore
a sequence-based detection method based on spatial-temporal correlation is introduced. For the detection of small moving infrared targets
prior information
such as the shape of small targets
the continuity of gray level change in time
and the continuity of moving track
is key to segment noise and small targets from infrared images effectively. Therefore
in accordance with the order of using these prior information
current mainstream infrared moving small-target detection methods are divided into two categories: detect before motion (DBM) and motion before detect (MBD). These two kinds of algorithms have different application ranges according to their own characteristics. The DBM method is relatively simple
easy to explement
and widely used in tasks with high real-time requirements. By contrast
the MBD method has high detection rate and low false alarm rate and can achieve good detection results in low signals to clutter ratio backgrounds. In this review
the principle
process
and characteristics of typical algorithms are introduced in detail
and the performance of each kind of detection algorithm is compared. At present
infrared small-target detection technologies may have reliable performance in short-term small-target detection and tracking tasks; however
the difficulty of small-target detection is prominent due to complex application scenarios
high requirements for long-term detection
and the particularity of target and background in practical applications. Therefore
according to the characteristics of infrared small targets
this work analyzes the difficulties of infrared small-target detection methods
provides solutions and shortcomings of various algorithms
and discusses the development direction of infrared small-target detection. Thus far
infrared small-target detection technologies have made remarkable progress and have been widely used in infrared guidance and antimissile tasks. However
infrared small-target detection technologies still suffer from some problems. For the characteristics of infrared small-target detection
we need to test and improve the detection theory of small targets further. To improve the detection effect of small targets in infrared images
we must constantly study the corresponding detection methods and improve the schemes. The application of infrared dim- and small-target detection is challenging and complex. The robustness and accuracy of the corresponding algorithms are constantly improved
and the detection speed is also required to meet real-time requirements. Combined with the application characteristics and scope of different military equipment
a universal overseas small-target detection algorithm should be studied. The algorithm should have high accuracy and robustness and must meet real-time requirements to enhance the all-weather reconnaissance capability and the target battlefield information collection capability of the equipment. Therefore
we can also summarize the major development directions of infrared small-target detection technology in the future. First
from the perspective of image fusion of different imaging systems
imaging quality is improved. Second
the existing algorithm is improved by combining the spatial-temporal information of images and the idea of iterative optimization. Third
several datasets are collected
and deep learning methods are explored to improve the accuracy of detection algorithm. Lastly
the improvements of hardware systems are used to accelerate the algorithm and improve the real-time detection. In the future
we will conduct corresponding research from these directions.
Achanta R, Hemami S, Estrada F and Susstrunk S. 2009. Frequency-tuned salient region detection//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Miami: IEEE: 1597-1604[ DOI:10.1109/CVPR.2009.5206596 http://dx.doi.org/10.1109/CVPR.2009.5206596 ]
Anju T S and Raj N R N. 2016. Shearlet transform based image denoising using histogram thresholding//Proceedings of 2016 International Conference on Communication Systems and Networks (ComNet). Thiruvananthapuram: IEEE: 162-166[ DOI:10.1109/CSN.2016.7824007 http://dx.doi.org/10.1109/CSN.2016.7824007 ]
Bai X Z and Bi Y G. 2018. Derivative entropy-based contrast measure for infrared small-target detection. IEEE Transactions on Geoscience and Remote Sensing, 56(4):2452-2466[DOI:10.1109/TGRS.2017.2781143]
Celik C and Bilge H S. 2017. Content based image retrieval with sparse representations and local feature descriptors:a comparative study. Pattern Recognition, 68:1-13[DOI:10.1016/j.patcog.2017.03.006]
Chen C L P, Li H, Wei Y T, Xia T and Tang Y Y. 2014. A local contrast method for small infrared target detection. IEEE Transactions on Geoscience and Remote Sensing, 52(1):574-581[DOI:10.1109/TGRS.2013.2242477]
Chen Z M, Tian M C, Bo Y M and Ling X D. 2018. Improved infrared small target detection and tracking method based on new intelligence particle filter. Computational Intelligence, 34(3):917-938[DOI:10.1111/coin.12150]
Chi J N, Zhang Z H, Wang D S and Wang Z L. 2007. Application of anti-symmetrical biorthogonal wavelet in infrared image small targets detection. Journal of Astronautics, 28(5):1253-1257, 1272
迟健男, 张朝晖, 王东署, 王志良. 2007.反对称双正交小波在红外图像小目标检测中的应用.宇航学报, 28(5):1253-1257, 1272)[DOI:10.3321/j.issn:1000-1328.2007.05.035]
Cui Z, Yang J L, Jiang S D and Li J B. 2016. An infrared small target detection algorithm based on high-speed local contrast method. Infrared Physics and Technology, 76:474-481[DOI:10.1016/j.infrared.2016.03.023]
Dai Y M, Wu Y Q and Song Y. 2016. Infrared small target and background separation via column-wise weighted robust principal component analysis. Infrared Physics and Technology, 77:421-430[DOI:10.1016/j.infrared.2016.06.021]
Dai Y M and Wu Y Q. 2017a. Reweighted infrared patch-tensor model with both nonlocal and local priors for single-frame small target detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(8):3752-3767[DOI:10.1109/JSTARS.2017.2700023]
Dai Y M, Wu Y Q, Song Y and Guo Y. 2017b. Non-negative infrared patch-image model:robust target-background separation via partial sum minimization of singular values. Infrared Physics and Technology, 81:182-194[DOI:10.1016/j.infrared.2017.01.009]
Deng H, Sun X P, Liu M L, Ye C H and Zhou X. 2016a. Infrared small-target detection using multiscale gray difference weighted image entropy. IEEE Transactions on Aerospace and Electronic Systems, 52(1):60-72[DOI:10.1109/TAES.2015.140878]
Deng H, Sun X P, Liu M L, Ye C H and Zhou X. 2016b. Small infrared target detection based on weighted local difference measure. IEEE Transactions on Geoscience and Remote Sensing, 54(7):4204-4214[DOI:10.1109/TGRS.2016.2538295]
Deng H, Sun X P, Liu M L, Ye C H and Zhou X. 2017. Entropy-based window selection for detecting dim and small infrared targets. Pattern Recognition, 61:66-77[DOI:10.1016/j.patcog.2016.07.036]
Deng Q Q, Lu H Z, Tao H M, Hu M F and Zhao F. 2019. Multi-scale convolutional neural networks for space infrared point objects discrimination. IEEE Access, 7:28113-28123[DOI:10.1109/ACCESS.2019.2898028]
Fan Z L, Bi D Y, Xiong L, Ma S P, He L Y and Ding W S. 2018. Dim infrared image enhancement based on convolutional neural network. Neurocomputing, 272:396-404[DOI:10.1016/j.neucom.2017.07.017]
Gao C Q, Meng D Y, Yang Y, Wang Y T, Zhou X F and Hauptmann A G. 2013. Infrared patch-image model for small target detection in a single image. IEEE Transactions on Image Processing, 22(12):4996-5009[DOI:10.1109/TIP.2013.2281420]
Gordon N J, Salmond D J and Smith A F M. 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F-Radar and Signal Processing, 140(2):107-113[DOI:10.1049/ip-f-2.1993.0015]
Han J H.2016. Infrared Small Target Detection under Complex Background Based on Human Visual System. Wuhan:Huazhong University of Science and Technology
韩金辉. 2016.基于人类视觉特性的复杂背景红外小目标检测研究.武汉:华中科技大学
Han J H, Liu S B, Qin G, Zhao Q, Zhang H H and Li N N. 2019. A local contrast method combined with adaptive background estimation for infrared small target detection. IEEE Geoscience and Remote Sensing Letters, 16(9):1442-1446[DOI:10.1109/LGRS.2019.2898893]
Han Y, Zhang P, Fei C and Wang X Y. 2015. Infrared small target detection based on spatio-temporal saliency in video sequence//Proceedings of the 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). Chengdu: IEEE: 279-282[ DOI:10.1109/ICCWAMTIP.2015.7493992 http://dx.doi.org/10.1109/ICCWAMTIP.2015.7493992 ]
Hao X R and Zhang Y Z. 2005. Method of moving point target detection in image sequences. Infrared and Laser Engineering, 34(6):709-713, 718
郝晓冉, 张有志. 2005.一种序列图像中运动点目标的检测方法.红外与激光工程, 34(6):709-713, 718)[DOI:10.3969/j.issn.1007-2276.2005.06.017]
He Y J, Li M, Zhang J L and An Q. 2015. Small infrared target detection based on low-rank and sparse representation. Infrared Physics and Technology, 68:98-109[DOI:10.1016/j.infrared.2014.10.022]
Hou X D and Zhang L Q. 2007. Saliency detection: a spectral residual approach//Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Minneapolis: IEEE: 1-8[ DOI:10.1109/CVPR.2007.383267 http://dx.doi.org/10.1109/CVPR.2007.383267 ]
Huan K W, Pang B, Shi X G, Zhao Q Y and Shi N N. 2008. Research on performance testing and evaluation of infrared imaging system. Infrared and Laser Engineering, 37(S2):482-486
宦克为, 庞博, 石晓光, 赵青义, 石宁宁. 2008, 红外成像系统的性能测试及评价方法研究.红外与激光工程, 37(S2):482-486
Huang H, Tao H J and Wang H F. 2019. Low-illumination image enhancement using a conditional generative adversarial network. Journal of Image and Graphics, 24(12):2149-2158
黄鐄, 陶海军, 王海峰. 2019.条件生成对抗网络的低照度图像增强方法.中国图象图形学报, 24(12):2149-2158)[DOI:10.11834/jig.190145]
Jiao J and Lingda W. 2017. Infrared dim small target detection method based on background prediction and high-order statistics//Proceedings of the 2nd International Conference on Image, Vision and Computing (ICIVC). Chengdu: IEEE: 53-57[ DOI:10.1109/ICIVC.2017.7984517 http://dx.doi.org/10.1109/ICIVC.2017.7984517 ]
Jiao P F. 2019. Research on Image Classification and Retrieval Method Based on Deep Learning and Sparse Representation. Xi'an: Xi'an University of Technology
焦鹏飞. 2019.基于深度学习和稀疏表示的图像分类与检索方法研究.西安: 西安理工大学
Jing P G, Su YT, Jin X and Zhang C Q. 2019. High-order temporal correlation model learning for time-series prediction. IEEE Transactions on Cybernetics, 49(6):2385-2397[DOI:10.1109/TCYB.2018.2832085]
Li Y S, Zhang Y J, Yu J G, Tan Y H, Tian J W and Ma J Y. 2016. A novel spatio-temporal saliency approach for robust dim moving target detection from airborne infrared image sequences. Information Sciences, 369:548-563[DOI:10.1016/j.ins.2016.07.042]
Liu M Q, Huang Z C, Fan Z, Zhang S L and He Y. 2017a. Infrared dim target detection and tracking based on particle filter//Proceedings of the 36th Chinese Control Conference (CCC). Dalian: IEEE: 5372-5378[ DOI:10.23919/ChiCC.2017.8028206 http://dx.doi.org/10.23919/ChiCC.2017.8028206 ]
Liu Q, Lu X H, He Z Y, Zhang C K and Chen W S. 2017b. Deep convolutional neural networks for thermal infrared object tracking. Knowledge-Based Systems, 134:189-198[DOI:10.1016/j.knosys.2017.07.032]
Lu H C, Li P X and Wang D. 2018. Visual objecttracking:a survey. Pattern Recognition and Artificial Intelligence, 31(1):61-76
卢湖川, 李佩霞, 王栋. 2018.目标跟踪算法综述.模式识别与人工智能, 31(1):61-76)[DOI:10.16451/j.cnki.issn1003-6059.201801006]
Luo J H, Ji H B and Liu J. 2007. An algorithm based on spatial filter for infrared small target detection and its application to an all directional IRST system//Proceedings of SPIE 6279, the 27th International Congress on High-Speed Photography and Photonics. Xi'an, China: SPIE: 6279(1): 62793E-627793E-6[ DOI:10.1117/12.725333 http://dx.doi.org/10.1117/12.725333 ]
Marvasti F S, Mosavi M R and Nasiri M. 2018. Flying small target detection in IR images based on adaptive toggle operator. IET Computer Vision, 12(4):527-534[DOI:10.1049/iet-cvi.2017.0327]
Nasrabadi N M. 2014. Hyperspectral target detection:an overview of current and future challenges. IEEE Signal Processing Magazine, 31(1):34-44[DOI:10.1109/MSP.2013.2278992]
Ni L. 2010. Wavelet Transform and Image Processing. Hefei:China University of Science and Technology Press
倪林. 2010.小波变换与图像处理.合肥:中国科学技术大学出版社
Qi S X, Ma J, Li H, Zhang S P and Tian J W. 2014. Infrared small target enhancement via phase spectrum of quaternion Fourier transform. Infrared Physics and Technology, 62:50-58[DOI:10.1016/j.infrared.2013.10.008]
Ren S Q, He K M, Girshick R and Sun J. 2017. Faster R-CNN:towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6):1137-1149[DOI:10.1109/TPAMI.2016.2577031]
Roth M W. 1989. Neural networks for extraction of weak targets in high clutter environments. IEEE Transactions on Systems, Man, and Cybernetics, 19(5):1210-1217[DOI:10.1109/21.44038]
Wang G, Chen Y G, Yang S C, Gao M and Dai Y P. 2015. Detection of infrared dim small target based on image patch contrast. Optics and Precision Engineering, 23(5):1424-1433
王刚, 陈永光, 杨锁昌, 高敏, 戴亚平. 2015.采用图像块对比特性的红外弱小目标检测.光学精密工程, 23(5):1424-1433)[DOI:10.3788/OPE.20152305.1424]
Wang G H, Mao S Y and He Y. 2002. A survey of radar and infrared data fusion. Fire Control and Command Control, 27(2):3-6
王国宏, 毛士艺, 何友. 2002.雷达与红外数据融合评述.火力与指挥控制, 27(2):3-6)[DOI:10.3969/j.issn.1002-0640.2002.02.001]
Wang H X, Dong H and Zhou Z Q. 2019. Review on dim small target detection technologies in infrared single frame images. Laser and Optoelectronics Progress, 56(8):#080001
王好贤, 董衡, 周志权. 2019.红外单帧图像弱小目标检测技术综述.激光与光电子学进展, 56(8):#080001)[DOI:10.3788/lop56.080001]
Wang X Y, Peng Z M, Kong D H and He Y M. 2017a. Infrared dim and small target detection based on stable multisubspace learning in heterogeneous scene. IEEE Transactions on Geoscience and Remote Sensing, 55(10):5481-5493[DOI:10.1109/TGRS.2017.2709250]
Wang X Y, Peng Z M, Kong D H, Zhang P and He Y M. 2017b. Infrared dim target detection based on total variation regularization and principal component pursuit. Image and Vision Computing, 63:1-9[DOI:10.1016/j.imavis.2017.04.002]
Wang X Y, Peng Z M, Zhang P and He Y M. 2017c. Infrared small target detection via nonnegativity-constrained variational mode decomposition. IEEE Geoscience and Remote Sensing Letters, 14(10):1700-1704[DOI:10.1109/LGRS.2017.2729512]
Wang X Y. 2018. Research on Infrared Dim and Small Target Detection Theory and Methodology Based on Sparse Dynamic Inversion. Chengdu: University of Electronic Science and Technology of China
王晓阳. 2018.基于稀疏动态反演的红外弱小目标检测理论及方法研究.成都: 电子科技大学
Wang Y H, Cen Y G, Zhao R Z, Cen Y, Hu S H, Voronin V and Wang H Y. 2017d. Separable vocabulary and feature fusion for image retrieval based on sparse representation. Neurocomputing, 236:14-22[DOI:10.1016/j.neucom.2016.08.106]
Wei Y T, You X G and Li H. 2016. Multiscale patch-based contrast measure for small infrared target detection. Pattern Recognition, 58:216-226[DOI:10.1016/j.patcog.2016.04.002]
Wright J. 2008. Robust face recognition via sparse representation. IEEE Transactions Pattern Analysis and Machine Intelligence, 31(2):210-227[DOI:10.1109/TPAMI.2008.79]
Wu T A, Huang S C, Yuan Z W, Wu Y R and Feng H. 2016. NSCT combined with SVD for infrared dim target complex background suppression. Infrared Technology, 38(9):758-764
吴天爱, 黄树彩, 苑智玮, 吴云荣, 冯卉. 2016.联合NSCT和SVD方法的红外弱小目标复杂背景抑制.红外技术, 38(9):758-764
Xiong B, Huang X H and Wang M. 2017. Infrared dim small target detection based on adaptive target image recovery. Journal of Huazhong University of Science and Technology (Nature Science Edition), 45(10):25-30
熊斌, 黄心汉, 王敏. 2017.基于自适应目标图像恢复的红外弱小目标检测.华中科技大学学报(自然科学版), 45(10):25-30)[DOI:10.13245/j.hust.171005]
Xiong W, Xu Y L, Yao L B, Cui Y Q and Li Y F. 2018. A new ship target detection algorithm based on visual salience calculation of spectral residuals in high-resolution SAR images. Electronics Optics and Control, 25(4):7-11, 49
熊伟, 徐永力, 姚力波, 崔亚奇, 李岳峰. 2018.基于频谱残差视觉显著计算的高分辨SAR图像舰船检测算法.电光与控制, 25(4):7-11, 49)[DOI:10.3969/j.issn.1671-637X.2018.04.002]
Yang C C, Ma J Y, Zhang M F, Zheng S and Tian X. 2014. Multiscale facet model for infrared small target detection. Infrared Physics and Technology, 67:202-209[DOI:10.1016/j.infrared.2014.07.029]
Yi X and Wang B J. 2017. Fast infrared and dim target detection algorithm based on multi-feature. Acta Photonica Sinica, 46(6):123-128
易翔, 王炳健. 2017.基于多特征的快速红外弱小目标检测算法.光子学报, 46(6):123-128)[DOI:10.3788/gzxb20174606.0610002]
Yi X, Wang B J, Zhou H X and Qin H L. 2019. Dim and small infrared target fast detection guided by visual saliency. Infrared Physics and Technology, 97:6-14[DOI:10.1016/j.infrared.2018.12.007]
Yu X, Reed I S, Kraske W and Stocker A D. 1992. A robust adaptive multi-spectral object detection by using wavelet transform//Proceedings of 1992 IEEE International Conference on Acoustics (ICA). San Francisco: IEEE: 141-144[ DOI:10.1109/ICASSP.1992.226638 http://dx.doi.org/10.1109/ICASSP.1992.226638 ]
Zhang H Y and Zhang T W. 2005. A new method based on multi-stages trace association for detection and tracking of crossing multi-targets. Acta Electronica Sinica, 33(6):1109-1112
张海英, 张田文. 2005.基于多阶段轨迹融合的交叉多目标检测与跟踪算法.电子学报, 33(6):1109-1112)[DOI:10.3321/j.issn:0372-2112.2005.06.038]
Zhang L and Liu H X. 2011. Optical system design of long wave infrared zoom lens. Infrared and Laser Engineering, 40(7):1279-1281, 1309
张良, 刘红霞. 2011.长波红外连续变焦光学系统的设计.红外与激光工程, 40(7):1279-1281, 1309)[DOI:10.3969/j.issn.1007-2276.2011.07.018]
Zhang L D, Peng L B, Zhang T F, Cao S Y and Peng Z M. 2018. Infrared small target detection via non-convex rank approximation minimization Joint/2, 1 norm. Remote Sensing, 10(11):1821[DOI:10.3390/rs10111821]
Zhang P, Wang X W, Wang X Y, Fei C and Guo Z K. 2019. Infrared small target detection based on spatial-temporal enhancement using quaternion discrete cosine transform. IEEE Access, 7:54712-54723[DOI:10.1109/ACCESS.2019.2912976]
Zhang X. 2016. Research on Image Classification Algorithm Based on Local Features and Feature Representation. Hefei: Hefei University of Technology
张旭. 2016.面向局部特征和特征表达的图像分类算法研究.合肥: 合肥工业大学
Zhang Y, Yong Y, Zhang Q H, Xu Z Y, Yan P and Wei Y X. 2010. Detection of dim point target with low contrast. High Power Laser and Particle Beams, 22(11):2566-2570
张耀, 雍杨, 张启衡, 徐智勇, 严棚, 魏宇星. 2010.低对比度小目标检测.强激光与粒子束, 22(11):2566-2570)[DOI:10.3788/HPLPB20102211.2566]
Zhao J J, Tang Z Y, Yang J and Liu E Q. 2011. Infrared small target detection using sparse representation. Journal of Systems Engineering and Electronics, 22(6):897-904[DOI:10.3969/j.issn.1004-4132.2011.06.004]
Zhao K and Kong X W. 2004. Background noise suppression in small targets infrared images and its method discussion. Optics and Optoelectronic Technology, 2(2):9-12
赵坤, 孔祥维. 2004.小目标红外图像背景噪声的抑制及方法讨论.光学与光电技术, 2(2):9-12)[DOI:10.3969/j.issn.1672-3392.2004.02.003]
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