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纸质出版日期： 2024-09-16 ，
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寇人可， 王春平， 罗迎， 张勇， 徐泽龙， 彭真明， 武晨燕， 付强. 2024. 单帧红外图像多尺度小目标检测技术综述. 中国图象图形学报， 29(09):2625-2649
Kou Renke， Wang Chunping， Luo Ying， Zhang Yong， Xu Zelong， Peng Zhenming， Wu Chenyan， Fu Qiang. 2024. Multiscale small-target detection techniques in single-frame infrared images： a review. Journal of Image and Graphics， 29(09):2625-2649
在复杂背景和噪声干扰下，如何利用红外探测系统快速且准确发现特征少、强度低、尺度变化以及运动状态未知的非合作小目标是一项具有挑战性的任务，备受学者关注。为了让读者全面了解该领域的研究现状，本综述将从算法原理、文献、数据集、评价指标、实验和发展方向等方面进行总结概括。首先，解释了以“红外多尺度小目标（点源和小面源）”为对象进行研究的原因并分析了红外多尺度小目标及背景的成像特性；其次，分别讨论了基于经典算法和深度学习算法的原理、设计策略和相关文献，并对比分析了这两类算法的优缺点；然后，总结了现有的红外小目标公开数据集和算法评价指标；最后，分别选取7种经典算法和15种深度学习算法进行定性和定量的对比分析。通过对单帧红外图像多尺度小目标检测技术的全面回顾，对该领域下一步的研究方向给出了9条具体建议。本综述不仅可以帮助初学者快速了解该领域的研究现状和发展趋势，也可作为其他研究的参考资料。此外，在本领域研究过程中，还将现有的20种经典算法、15种深度学习算法和9种评价指标集成在人机交互系统中，相关系统的视频介绍发布可由以下链接得到：
https://github.com/kourenke/GUI-system-for-infrared-small-target-detection
https://github.com/kourenke/GUI-system-for-infrared-small-target-detection
。
Traditional radar detection is almost ineffective in complex and strong electromagnetic interference environments， especially in the case of stealth targets with an extremely low-radar cross-section. In such scenarios， the infrared search and track （IRST） system， with its strong anti-interference capability and all-weather and all-airspace passive detection， emerges as a viable alternative to radar in target detection. Therefore， this system is widely used， such as reconnaissance and early warning， maritime surveillance， and precision guidance. However， the efficient and accurate use of the IRST system when identifying noncooperative small targets with minimal features， low intensity， scale variations， and unknown motion states in complex backgrounds and amidst noise interference remains a challenging task， which draws attention from scholars globally. To date， the research on infrared （IR） small-target detection technology mainly focuses on long-distance weak and small （point source） targets. However， when the scene and target scale change considerably， false alarms or missed detections easily occur. Therefore， this review focuses on the problem of IR multiscale small-target detection technology. To provide a comprehensive understanding of the current research status in this domain， this review summarizes the field from the perspectives of algorithm principles， literature， datasets， evaluation metrics， experiments， and development directions. First， the research motivation is clarified. In practical application background， with the change in the motion state of noncooperative small targets， the scale also vary greatly， from point-like targets to small targets with fuzzy boundaries to small targets with clear outlines， which is usually difficult to distinguish properly. Therefore， to be more in line with practical application background， this review is comprehensively analyzed from the perspective of IR multiscale small-target detection technology. Second， the imaging characteristics of IR multiscale small targets and backgrounds are analyzed. The targets are characterized by various types， scale changes （from point sources to small surface sources）， low intensity， fuzzy boundaries， lack of texture and color information， and unknown motion status. The background also exhibits characterization of by complex and variable scenes and serious noise interference. Then， the algorithm principles， related literature， and advantages and disadvantages of different algorithms for single-frame IR image using multiscale small-target detection techniques are summarized. In this review， we classify IR multiscale small-target single-frame detection techniques into two main categories： classical and deep learning algorithms. The former are classified into background estimation， morphological， directional derivative/gradient/entropy， local contrast， frequency-domain， overcomplete sparse representation， and sparse low-rank decomposition methods based on various modeling ideas. The latter are divided into convolutional neural networks （CNNs）， classical algorithm + CNN， and CNN + Transformer based on network structure. In these network structures， for the adequate extraction of the IR multiscale small-target feature information， design strategies， such as contextual feature fusion， multi-scale feature fusion， dense nesting， and generative adversarial networks， have been introduced. To reduce the computational complexity or the limitation of data sample size， scholars introduced strategies， such as lightweight design and weak supervision. Classical algorithms and deep learning algorithms feature their on advantages and disadvantages， and thus， appropriate algorithms should be selected depending on specific problems and needs. In addition， the combination of the two types of algorithms to maximize their advantages is a current research hotspot. Finally， 10 existing public datasets and 17 evaluation metrics are organized， and 7 classical algorithms and 15 deep learning algorithms are selected for qualitative and quantitative comparative analysis. In addition， in the research process in this field， we have integrated 20 existing classical algorithms， 15 deep learning algorithms and 9 evaluation metrics in a human-computer interaction system， and the video introduction of the relevant system is published in kourenke/GUI-system-for-IR-small-target-detection （github.com）. A comprehensive review of multiscale small-target detection techniques in single frame IR images resulted in 9 specific suggestions for subsequent research directions in this field. This review cannot only help beginners in rapidly comprehending the research status and development trends in this field but also serve as a reference material for other researchers.
红外图像多尺度小目标目标检测经典算法深度学习算法
infrared imagemulti-scale small targettarget detectionclassical algorithmdeep learning algorithm
Aghaziyarati S， Moradi S and Talebi H. 2019. Small infrared target detection using absolute average difference weighted by cumulative directional derivatives. Infrared Physics and Technology， 101： 78-87 ［DOI： 10.1016/j.infrared.2019.06.003http://dx.doi.org/10.1016/j.infrared.2019.06.003］
Bae T W and Sohng K I. 2010. Small target detection using bilateral filter based on edge component. Journal of Infrared， Millimeter， and Terahertz Waves， 31（6）： 735-743 ［DOI： 10.1007/s10762-010-9633-0http://dx.doi.org/10.1007/s10762-010-9633-0］
Bai X Z and Zhou F G. 2010. Analysis of new top-hat transformation and the application for infrared dim small target detection. Pattern Recognition， 43（6）： 2145-2156 ［DOI： 10.1016/j.patcog.2009.12.023http://dx.doi.org/10.1016/j.patcog.2009.12.023］
Bi Y G， Chen J Z， Sun H and Bai X Z. 2020. Fast detection of distant， infrared targets in a single image using multiorder directional derivatives. IEEE Transactions on Aerospace and Electronic Systems， 56（3）： 2422-2436 ［DOI： 10.1109/TAES.2019.2946678http://dx.doi.org/10.1109/TAES.2019.2946678］
Bochkovskiy A， Wang C Y and Liao H Y M. 2020. YOLOv4： optimal speed and accuracy of object detection ［EB/OL］. ［2023-11-01］. https://arxiv.org/pdf/2004.10934.pdfhttps://arxiv.org/pdf/2004.10934.pdf
Cao Y， Liu R M and Yang J. 2008. Small target detection using two-dimensional least mean square （TDLMS） filter based on neighborhood analysis. International Journal of Infrared and Millimeter Waves， 29（2）： 188-200 ［DOI： 10.1007/s10762-007-9313-xhttp://dx.doi.org/10.1007/s10762-007-9313-x］
Chapple P B， Bertilone D C， Caprari R S， Angeli S and Newsam G N. 1999. Target detection in infrared and SAR terrain images using a non-Gaussian stochastic model//Proceedings Volume 3699， Targets and Backgrounds： Characterization and Representation V. Orlando， United States： SPIE： 122-132 ［DOI： 10.1117/12.352951http://dx.doi.org/10.1117/12.352951］
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.2013.2242477http://dx.doi.org/10.1109/TGRS.2013.2242477］
Chen F， Gao C Q， Liu F C， Zhao Y， Zhou Y X， Meng D Y and Zuo W M. 2022a. Local patch network with global attention for infrared small target detection. IEEE Transactions on Aerospace and Electronic Systems， 58（5）： 3979-3991 ［DOI： 10.1109/TAES.2022.3159308http://dx.doi.org/10.1109/TAES.2022.3159308］
Chen L C， Papandreou G， Kokkinos I， Murphy K and Yuille A L. 2018a. DeepLab： semantic image segmentation with deep convolutional nets， atrous convolution， and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence， 40（4）： 834-848 ［DOI： 10.1109/TPAMI.2017.2699184http://dx.doi.org/10.1109/TPAMI.2017.2699184］
Chen L C， Zhu Y K， Papandreou G， Schroff F and Adam H. 2018b. Encoder-decoder with atrous separable convolution for semantic image segmentation//Proceedings of the 15th European Conference on Computer Vision. Munich， Germany： Springer： 833-851 ［DOI： 10.1007/978-3-030-01234-2_49http://dx.doi.org/10.1007/978-3-030-01234-2_49］
Chen Y H， Li L Y， Liu X and Su X F. 2022b. A multi-task framework for infrared small target detection and segmentation. IEEE Transactions on Geoscience and Remote Sensing， 60： #5003109 ［DOI： 10.1109/TGRS.2022.3195740http://dx.doi.org/10.1109/TGRS.2022.3195740］
Dai Y M， Li X， Zhou F， Qian Y L， Chen Y H and Yang J. 2023. One-stage cascade refinement networks for infrared small target detection. IEEE Transactions on Geoscience and Remote Sensing， 61： #5000917 ［DOI： 10.1109/TGRS.2023.3243062http://dx.doi.org/10.1109/TGRS.2023.3243062］
Dai Y M and Wu Y Q. 2017. 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.2017.2700023http://dx.doi.org/10.1109/JSTARS.2017.2700023］
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.2016.06.021http://dx.doi.org/10.1016/j.infrared.2016.06.021］
Dai Y M， Wu Y Q， Song Y and Guo J. 2017. 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.2017.01.009http://dx.doi.org/10.1016/j.infrared.2017.01.009］
Dai Y M， Wu Y Q， Zhou F and Barnard K. 2021a. Attentional local contrast networks for infrared small target detection. IEEE Transactions on Geoscience and Remote Sensing， 59（11）： 9813-9824 ［DOI： 10.1109/TGRS.2020.3044958http://dx.doi.org/10.1109/TGRS.2020.3044958］
Dai Y M， Wu Y Q， Zhou F and Barnard K. 2021b. Asymmetric contextual modulation for infrared small target detection//Proceedings of 2021 IEEE Winter Conference on Applications of Computer Vision （WACV）. Waikoloa， USA： IEEE： 949-958 ［DOI： 10.1109/WACV48630.2021.00099http://dx.doi.org/10.1109/WACV48630.2021.00099］
Deng H， Sun X P， Liu M L， Ye C H and Zhou X. 2016. 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.2015.140878http://dx.doi.org/10.1109/TAES.2015.140878］
Deng L Z， Zhang J K， Xu G X and Zhu H. 2021. Infrared small target detection via adaptive M-estimator ring top-hat transformation. Pattern Recognition， 112： #107729 ［DOI： 10.1016/j.patcog.2020.107729http://dx.doi.org/10.1016/j.patcog.2020.107729］
Deshpande S D， Er M H， Venkateswarlu R and Chan P. 1999. Max-mean and max-median filters for detection of small targets//Proceedings Volume 3809， Signal and Data Processing of Small Targets. Denver， United States： SPIE： 74-83 ［DOI： 10.1117/12.364049http://dx.doi.org/10.1117/12.364049］
Dosovitskiy A， Beyer L， Kolesnikov A， Weissenborn D， Zhai X H， Unterthiner T， Dehghani M， Minderer M， Heigold G， Gelly S， Uszkoreit J and Houlsby N. 2021. An image is worth 16x16 words： Transformers for image recognition at scale ［EB/OL］. ［2023-11-01］. https://arxiv.org/pdf/2010.11929. pdfhttps://arxiv.org/pdf/2010.11929.pdf
Fang H Z， Ding L， Wang L M， Chang Y， Yan L X and Han J H. 2022. Infrared small UAV target detection based on depthwise separable residual dense network and multiscale feature fusion. IEEE Transactions on Instrumentation and Measurement， 71： #5019120 ［DOI： 10.1109/TIM.2022.3198490http://dx.doi.org/10.1109/TIM.2022.3198490］
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.2013.2281420http://dx.doi.org/10.1109/TIP.2013.2281420］
Girshick R， Donahue J， Darrell T and Malik J. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus， USA： IEEE： 580-587 ［DOI： 10.1109/CVPR.2014.81http://dx.doi.org/10.1109/CVPR.2014.81］
Han J H， Liang K， Zhou B， Zhu X Y， Zhao J and Zhao L L. 2018. Infrared small target detection utilizing the multiscale relative local contrast measure. IEEE Geoscience and Remote Sensing Letters， 15（4）： 612-616 ［DOI： 10.1109/LGRS.2018.2790909http://dx.doi.org/10.1109/LGRS.2018.2790909］
Han J H， Liu C Y， Liu Y C， Luo Z， Zhang X J and Niu Q F. 2021. Infrared small target detection utilizing the enhanced closest-mean background estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 14： 645-662 ［DOI： 10.1109/JSTARS.2020.3038442http://dx.doi.org/10.1109/JSTARS.2020.3038442］
Han J H， Ma Y， Zhou B， Fan F， Liang K and Fang Y. 2014. A robust infrared small target detection algorithm based on human visual system. IEEE Geoscience and Remote Sensing Letters， 11（12）： 2168-2172 ［DOI： 10.1109/LGRS.2014.2323236http://dx.doi.org/10.1109/LGRS.2014.2323236］
Han J H， Moradi S， Faramarzi I， Liu C Y， Zhang H H and Zhao Q. 2020. A local contrast method for infrared small-target detection utilizing a tri-layer window. IEEE Geoscience and Remote Sensing Letters， 17（10）： 1822-1826 ［DOI： 10.1109/LGRS.2019.2954578http://dx.doi.org/10.1109/LGRS.2019.2954578］
Han J H， Wei Y T， Peng Z M， Zhao Q， Chen Y H， Qin Y and Li N. 2022. Infrared dim and small target detection： a review. Infrared and Laser Engineering， 51（4）： #20210393
韩金辉， 魏艳涛， 彭真明， 赵骞， 陈耀弘， 覃尧， 李楠. 2022. 红外弱小目标检测方法综述. 红外与激光工程， 51（4）： #20210393 ［DOI： 10.3788/IRLA20210393http://dx.doi.org/10.3788/IRLA20210393］
He X， Ling Q， Zhang Y Y， Lin Z P and Zhou S L. 2022. Detecting dim small target in infrared images via subpixel sampling cuneate network. IEEE Geoscience and Remote Sensing Letters， 19： #6513005 ［DOI： 10.1109/LGRS.2022.3189225http://dx.doi.org/10.1109/LGRS.2022.3189225］
Hou Q Y， Wang Z P， Tan F J， Zhao Y， Zheng H L and Zhang W. 2022. RISTDnet： robust infrared small target detection network. IEEE Geoscience and Remote Sensing Letters， 19： #7000805 ［DOI： 10.1109/LGRS.2021.3050828http://dx.doi.org/10.1109/LGRS.2021.3050828］
Hu J， Shen L and Sun G. 2018. Squeeze-and-excitation networks//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake， USA： IEEE： 7132-7141 ［DOI： 10.1109/CVPR.2018.00745http://dx.doi.org/10.1109/CVPR.2018.00745］
Hu K， Sun W H， Nie Z B， Cheng R， Chen S and Kang Y. 2022. Real-time infrared small target detection network and accelerator design. Integration， 87： 241-252 ［DOI： 10.1016/j.vlsi.2022.07.008http://dx.doi.org/10.1016/j.vlsi.2022.07.008］
Huang L， Dai S S， Huang T， Huang X K and Wang H N. 2021. Infrared small target segmentation with multiscale feature representation. Infrared Physics and Technology， 116： #103755 ［DOI： 10.1016/j.infrared.2021.103755http://dx.doi.org/10.1016/j.infrared.2021.103755］
Huang S Q. 2020. Infrared Dim Small Target Detection Based on Joint Temporal-Spatial-Spectral Features. Chengdu： University of Electronic Science and Technology of China （黄苏琦. 2020. 时空谱多特征联合红外弱小目标检测方法研究. 成都： 电子科技大学）
Hui B W， Song Z Y， Fan H Q， Zhong P， Hu W D， Zhang X F， Ling J G， Su H Y， Jin W， Zhang Y J and Bai Y X. 2020. A dataset for infrared detection and tracking of dim-small aircraft targets under ground/air background. China Scientific Data， 5（3）： 286-297
回丙伟， 宋志勇， 范红旗， 钟平， 胡卫东， 张晓峰， 凌建国， 苏宏艳， 金威， 张永杰， 白亚茜. 2020. 地/空背景下红外图像弱小飞机目标检测跟踪数据集. 中国科学数据， 5（3）： 286-297 ［DOI： 10.11922/csdata.2019.0074.zhhttp://dx.doi.org/10.11922/csdata.2019.0074.zh］
Jiang N， Wang K R， Peng X K， Yu X H， Wang Q， Xing J L， Li G R， Zhao J， Guo G D and Han Z J. 2021. Anti-UAV： a large multi-modal benchmark for UAV tracking ［EB/OL］. ［2023-11-01］. https://arxiv.org/pdf/2101.08466.pdfhttps://arxiv.org/pdf/2101.08466.pdf
Ju M R， Luo J N， Liu G Q and Luo H B. 2021. ISTDet： an efficient end-to-end neural network for infrared small target detection. Infrared Physics and Technology， 114： #103659 ［DOI： 10.1016/j.infrared.2021.103659http://dx.doi.org/10.1016/j.infrared.2021.103659］
Kim J H and Hwang Y. 2022. GAN-based synthetic data augmentation for infrared small target detection. IEEE Transactions on Geoscience and Remote Sensing， 60： #5002512 ［DOI： 10.1109/TGRS.2022.3179891http://dx.doi.org/10.1109/TGRS.2022.3179891］
Kong X Y， Liu L， Qian Y S and Cui M J. 2016. Automatic detection of sea-sky horizon line and small targets in maritime infrared imagery. Infrared Physics and Technology， 76： 185-199 ［DOI： 10.1016/j.infrared.2016.01.016http://dx.doi.org/10.1016/j.infrared.2016.01.016］
Kou R K， Wang H Y， Zhao Z H and Wang F. 2017. Optimum selection of detection point and threshold noise ratio of airborne infrared search and track systems. Applied Optics， 56（18）： #5268 ［DOI： 10.1364/AO.56.005268http://dx.doi.org/10.1364/AO.56.005268］
Kou R K， Wang C P， Fu Q， Yu Y and Zhang D D. 2022. Infrared small target detection based on the improved density peak global search and human visual local contrast mechanism. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 15： 6144-6157 ［DOI： 10.1109/JSTARS.2022.3193884http://dx.doi.org/10.1109/JSTARS.2022.3193884］
Kou R K， Wang C P， Fu Q， Zhang J J and Huang F Y. 2023a. Detection model and performance evaluation for the infrared search and tracking system. Applied Optics， 62（2）： 398-410 ［DOI： 10.1364/AO.469807http://dx.doi.org/10.1364/AO.469807］
Kou R K， Wang C P， Peng Z M， Zhao Z H， Chen Y H， Han J H， Huang F Y， Yu Y and Fu Q. 2023b. Infrared small target segmentation networks： a survey. Pattern Recognition， 143： #109788 ［DOI： 10.1016/j.patcog.2023.109788http://dx.doi.org/10.1016/j.patcog.2023.109788］
Kou R K， Wang C P， Yu Y， Peng Z M， Yang M B， Huang F Y and Fu Q. 2023c. LW-IRSTNet： lightweight infrared small target segmentation network and application deployment. IEEE Transactions on Geoscience and Remote Sensing， 61： #5621313 ［DOI： 10.1109/TGRS.2023.3314586http://dx.doi.org/10.1109/TGRS.2023.3314586］
Kou R K， Wang C P， Yu Y， Peng Z M， Huang F Y and Fu Q. 2023d. Infrared small target tracking algorithm via segmentation network and multistrategy fusion. IEEE Transactions on Geoscience and Remote Sensing， 61： #5612912 ［DOI： 10.1109/TGRS.2023.3286836http://dx.doi.org/10.1109/TGRS.2023.3286836］
Kou R K， Wang C P， Fu Q， Li Z W， Luo Y， Li B Y， Li W， and Peng Z M. 2023e. MCGC： a multiscale chain growth clustering algorithm for generating infrared small target mask under single-point supervision. IEEE Transactions on Geoscience and Remote Sensing， 62： #5620412 ［DOI： 10.1109/TGRS.2024.3390756http://dx.doi.org/10.1109/TGRS.2024.3390756］
Li B Y， Wang Y Q， Wang L G， Zhang F， Liu T， Liu Z P， An W and Guo Y L. 2023a. Monte Carlo linear clustering with single-point supervision is enough for infrared small target detection//Proceedings of 2023 IEEE/CVF International Conference on Computer Vision. Paris， France： IEEE： 1009-1019 ［DOI： 10.1109/ICCV51070.2023.00099http://dx.doi.org/10.1109/ICCV51070.2023.00099］
Li B Y， Xiao C， Wang L G， Wang Y Q， Lin Z P， Li M， An W and Guo Y L. 2023b. Dense nested attention network for infrared small target detection. IEEE Transactions on Image Processing， 32： 1745-1758 ［DOI： 10.1109/TIP.2022.3199107http://dx.doi.org/10.1109/TIP.2022.3199107］
Li G， Yun I， Kim J and Kim J. 2019a. DABNet： depth-wise asymmetric bottleneck for real-time semantic segmentation ［EB/OL］. ［2023-11-01］. https://arxiv.org/pdf/1907.11357.pdfhttps://arxiv.org/pdf/1907.11357.pdf
Li H C， Xiong P F， Fan H Q and Sun J. 2019b. DFANet： deep feature aggregation for real-time semantic segmentation//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach， USA： IEEE： 9514-9523 ［DOI： 10.1109/CVPR.2019.00975http://dx.doi.org/10.1109/CVPR.2019.00975］
Li J H， Zhang P， Wang X W and Huang S Z. 2020. Infrared small-target detection algorithms： a survey. Journal of Image and Graphics， 25（9）： 1739-1753
李俊宏， 张萍， 王晓玮， 黄世泽. 2020. 红外弱小目标检测算法综述. 中国图象图形学报， 25（9）： 1739-1753 ［DOI： 10.11834/jig.190574http://dx.doi.org/10.11834/jig.190574］
Li Y， Li P C and Shen Q. 2014a. Real-time infrared target tracking based on ℓ1 minimization and compressive features. Applied Optics， 53（28）： #6518 ［DOI： 10.1364/AO.53.006518http://dx.doi.org/10.1364/AO.53.006518］
Li Z Z， Chen J， Hou Q， Fu H X， Dai Z， Jin G， Li R Z and Liu C J. 2014b. Sparse representation for infrared dim target detection via a discriminative over-complete dictionary learned online. Sensors， 14（6）： 9451-9470 ［DOI： 10.3390/s140609451http://dx.doi.org/10.3390/s140609451］
Lin T Y， Dollár P， Girshick R， He K M， Hariharan B and Belongie S. 2017. Feature pyramid networks for object detection//Proceedings of 2017 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Honolulu， HI， USA： IEEE： 2117-2125 ［DOI： 10.1109/CVPR.2017.106http://dx.doi.org/10.1109/CVPR.2017.106］
Liu D P， Li Z Z， Liu B， Chen W H， Liu T M and Cao L. 2017. Infrared small target detection in heavy sky scene clutter based on sparse representation. Infrared Physics and Technology， 85： 13-31 ［DOI： 10.1016/j.infrared.2017.05.009http://dx.doi.org/10.1016/j.infrared.2017.05.009］
Liu F C， Gao C Q， Chen F， Meng D Y， Zuo W M and Gao X B. 2021b. Infrared small-dim target detection with Transformer under complex backgrounds ［EB/OL］. ［2023-11-01］. https://arxiv.org/pdf/2109.14379.pdfhttps://arxiv.org/pdf/2109.14379.pdf
Liu S， Chen P F and Woźniak M. 2022. Image enhancement-based detection with small infrared targets. Remote Sensing， 14（13）： #3232 ［DOI： 10.3390/rs14133232http://dx.doi.org/10.3390/rs14133232］
Liu W， Anguelov D， Erhan D， Szegedy C， Reed S， Fu C Y and Berg A C. 2016. SSD： single shot MultiBox detector//Proceedings of the 14th European Conference on Computer Vision. Amsterdam， the Netherlands： Springer： 21-37 ［DOI： 10.1007/978-3-319-46448-0_2http://dx.doi.org/10.1007/978-3-319-46448-0_2］
Liu Z， Lin Y T， Cao Y， Hu H， Wei Y X， Zhang Z， Lin S and Guo B N. 2021a. Swin Transformer： hierarchical vision Transformer using shifted windows//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 9992-10002 ［DOI： 10.1109/ICCV48922.2021.00986http://dx.doi.org/10.1109/ICCV48922.2021.00986］
Luo J H and Yu H. 2023. Research of infrared dim and small target detection algorithms based on low-rank and sparse decomposition. Laser and Optoelectronics Progress， 60（16）： #1600004
罗俊海， 余杭. 2023. 基于低秩稀疏分解的红外弱小目标检测算法研究进展. 激光与光电子学进展， 60（16）： #1600004 ［DOI： 10.3788/LOP222077http://dx.doi.org/10.3788/LOP222077］
McIntosh B， Venkataramanan S and Mahalanobis A. 2021. Infrared target detection in cluttered environments by maximization of a target to clutter ratio （TCR） metric using a convolutional neural network. IEEE Transactions on Aerospace and Electronic Systems， 57（1）： 485-496 ［DOI： 10.1109/TAES.2020.3024391http://dx.doi.org/10.1109/TAES.2020.3024391］
Pan P W， Wang H， Wang C Y and Nie C. 2023. ABC： attention with bilinear correlation for infrared small target detection//Proceedings of 2023 IEEE International Conference on Multimedia and Expo （ICME）. Brisbane， Australia： IEEE： 2381-2386 ［DOI： 10.1109/ICME55011.2023.00406http://dx.doi.org/10.1109/ICME55011.2023.00406］
Paszke A， Chaurasia A， Kim S and Culurciello E. 2016. ENet： a deep neural network architecture for real-time semantic segmentation ［EB/OL］. ［2023-11-01］. https://arxiv.org/pdf/1606.02147.pdfhttps://arxiv.org/pdf/1606.02147.pdf
Qin H L， Han J J， Yan X， Zeng Q J， Zhou H X， Li J and Chen Z M. 2016. Infrared small moving target detection using sparse representation-based image decomposition. Infrared Physics and Technology， 76： 148-156 ［DOI： 10.1016/j.infrared.2016.02.003http://dx.doi.org/10.1016/j.infrared.2016.02.003］
Qin Y and Li B. 2016. Effective infrared small target detection utilizing a novel local contrast method. IEEE Geoscience and Remote Sensing Letters， 13（12）： 1890-1894 ［DOI： 10.1109/LGRS.2016.2616416http://dx.doi.org/10.1109/LGRS.2016.2616416］
Quan T M， Hildebrand D G C and Jeong W K. 2021. FusionNet： a deep fully residual convolutional neural network for image segmentation in connectomics. Frontiers in Computer Science， 3： #613981 ［DOI： 10.3389/fcomp.2021.613981http://dx.doi.org/10.3389/fcomp.2021.613981］
Redmon J， Divvala S， Girshick R and Farhadi A. 2016. You only look once： unified， real-time object detection//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas， USA： IEEE： 779-788 ［DOI： 10.1109/CVPR.2016.91http://dx.doi.org/10.1109/CVPR.2016.91］
Redmon J and Farhadi A. 2018. YOLOv3： an incremental improvement ［EB/OL］. ［2023-11-01］. https://arxiv.org/pdf/1804.02767.pdfhttps://arxiv.org/pdf/1804.02767.pdf
Ren K， Gao Y， Wan M J， Gu G H and Chen Q. 2022. Infrared small target detection via region super resolution generative adversarial network. Applied Intelligence， 52（10）： 11725-11737 ［DOI： 10.1007/s10489-021-02955-6http://dx.doi.org/10.1007/s10489-021-02955-6］
Ronneberger O， Fischer P and Brox T. 2015. U-Net： convolutional networks for biomedical image segmentation//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich， Germany： Springer： 234-241 ［DOI： 10.1007/978-3-319-24574-4_28http://dx.doi.org/10.1007/978-3-319-24574-4_28］
Shao B， Yang H， Zhu B， Chen Y and Zou R P. 2023. Infrared small target detection algorithm based on real-time semantic segmentation. Laser and Optoelectronics Progress， 60（14）： #1410006
邵斌， 杨华， 朱斌， 陈熠， 邹融平. 2023. 基于实时语义分割的红外小目标检测算法. 激光与光电子学进展， 60（14）： #1410006 ［DOI： 10.3788/LOP221958http://dx.doi.org/10.3788/LOP221958］
Shelhamer E， Long J and Darrell T. 2017. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence， 39（4）： 640-651 ［DOI： 10.1109/TPAMI.2016.2572683http://dx.doi.org/10.1109/TPAMI.2016.2572683］
Sun H， Bai J X， Yang F and Bai X Z. 2023. Receptive-field and direction induced attention network for infrared dim small target detection with a large-scale dataset IRDST. IEEE Transactions on Geoscience and Remote Sensing， 61： #5000513 ［DOI： 10.1109/TGRS.2023.3235150http://dx.doi.org/10.1109/TGRS.2023.3235150］
Sun X L， Guo L C， Zhang W L， Wang Z and Yu Q F. 2021. Small aerial target detection for airborne infrared detection systems using lightGBM and trajectory constraints. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 14： 9959-9973 ［DOI： 10.1109/JSTARS.2021.3115637http://dx.doi.org/10.1109/JSTARS.2021.3115637］
Tom V T， Peli T， Leung M and Bondaryk J E. 1993. Morphology-based algorithm for point target detection in infrared backgrounds//Proceedings Volume 1954， Signal and Data Processing of Small Targets. Orlando， United States： SPIE： 2-11 ［DOI： 10.1117/12.157758http://dx.doi.org/10.1117/12.157758］
Tong X Z， Sun B， Wei J Y， Zuo Z and Su S J. 2021. EAAU-Net： enhanced asymmetric attention U-Net for infrared small target detection. Remote Sensing， 13（16）： #3200 ［DOI： 10.3390/rs13163200http://dx.doi.org/10.3390/rs13163200］
Wang A， Li W， Huang Z C， Wu X， Jie F R and Tao R. 2022a. Prior-guided data augmentation for infrared small target detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 15： 10027-10040 ［DOI： 10.1109/JSTARS.2022.3222758http://dx.doi.org/10.1109/JSTARS.2022.3222758］
Wang H， Zhou L P and Wang L. 2019. Miss detection vs. false alarm： adversarial learning for small object segmentation in infrared images//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision （ICCV）. Seoul， Korea （South）： IEEE： 8508-8517 ［DOI： 10.1109/ICCV.2019.00860http://dx.doi.org/10.1109/ICCV.2019.00860］
Wang K W， Du S Y， Liu C X and Cao Z G. 2022b. Interior attention-aware network for infrared small target detection. IEEE Transactions on Geoscience and Remote Sensing， 60： #5002013 ［DOI： 10.1109/TGRS.2022.3163410http://dx.doi.org/10.1109/TGRS.2022.3163410］
Wang X， Lv G F and Xu L Z. 2012. Infrared dim target detection based on visual attention. Infrared Physics and Technology， 55（6）： 513-521 ［DOI： 10.1016/j.infrared.2012.08.004http://dx.doi.org/10.1016/j.infrared.2012.08.004］
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.2017.2709250http://dx.doi.org/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.2017.04.002http://dx.doi.org/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.2017.2729512http://dx.doi.org/10.1109/LGRS.2017.2729512］
Wu T H， Li B Y， Luo Y H， Wang Y Q， Xiao C， Liu T， Yang J G， An W and Guo Y L. 2023. MTU-Net： multilevel TransUNet for space-based infrared tiny ship detection. IEEE Transactions on Geoscience and Remote Sensing， 61： #5601015 ［DOI： 10.1109/TGRS.2023.3235002http://dx.doi.org/10.1109/TGRS.2023.3235002］
Wu T Y， Tang S， Zhang R， Cao J and Zhang Y D. 2021. CGNet： a light-weight context guided network for semantic segmentation. IEEE Transactions on Image Processing， 30： 1169-1179 ［DOI： 10.1109/TIP.2020.3042065http://dx.doi.org/10.1109/TIP.2020.3042065］
Xi T Y， Yuan L H and Wang S P. 2023. Adaptive Top-Hat infrared small target detection based on local contrast. Laser and Optoelectronics Progress， 60（16）： #1628003
习腾彦， 袁丽华， 王树鹏. 2023. 基于局部对比度的自适应Top-Hat红外小目标检测. 激光与光电子学进展， 60（16）： #1628003 ［DOI： 10.3788/LOP222850http://dx.doi.org/10.3788/LOP222850］
Yang B C， Song W N， Jin H B and Li S M. 2023. Infrared small target detection using gradient differential anisotropic Gaussian filtering. Laser and Optoelectronics Progress， 60（16）： #1612003
杨本臣， 宋婉妮， 金海波， 李斯萌. 2023. 梯度差各向异性高斯滤波的红外小目标检测. 激光与光电子学进展， 60（16）： #1612003 ［DOI： 10.3788/LOP222723http://dx.doi.org/10.3788/LOP222723］
Yang L， Yang J and Yang K. 2004. Adaptive detection for infrared small target under sea-sky complex background. Electronics Letters， 40（17）： #1083 ［DOI： 10.1049/el：20045204http://dx.doi.org/10.1049/el：20045204］
Yao S B， Zhu Q Y， Zhang T， Cui W N and Yan P M. 2022. Infrared image small-target detection based on improved FCOS and spatio-temporal features. Electronics， 11（6）： #933 ［DOI： 10.3390/electronics11060933http://dx.doi.org/10.3390/electronics11060933］
Ying X Y， Liu L， Wang Y Q， Li R J， Chen N， Lin Z P， Sheng W D and Zhou S L. 2023. Mapping degeneration meets label evolution： learning infrared small target detection with single point supervision//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 15528-15538 ［DOI： 10.1109/CVPR52729.2023.01490http://dx.doi.org/10.1109/CVPR52729.2023.01490］
Yu C， Liu Y P， Wu S H， Hu Z H， Xia X， Lan D Y and Liu X. 2022a. Infrared small target detection based on multiscale local contrast learning networks. Infrared Physics and Technology， 123： #104107 ［DOI： 10.1016/j.infrared.2022.104107http://dx.doi.org/10.1016/j.infrared.2022.104107］
Yu C， Liu Y P， Wu S H， Xia X， Hu Z H， Lan D Y and Liu X. 2022b. Pay attention to local contrast learning networks for infrared small target detection. IEEE Geoscience and Remote Sensing Letters， 19： #3512705 ［DOI： 10.1109/LGRS.2022.3178984http://dx.doi.org/10.1109/LGRS.2022.3178984］
Yu C Q， Wang J B， Peng C， Gao C X， Yu G and Sang N. 2018a. Learning a discriminative feature network for semantic segmentation//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake， USA： IEEE： 1857-1866 ［DOI： 10.1109/CVPR.2018.00199http://dx.doi.org/10.1109/CVPR.2018.00199］
Yu C Q， Wang J B， Peng C， Gao C X， Yu G and Sang N. 2018b. BiSeNet： bilateral segmentation network for real-time semantic segmentation//Proceedings of the 15th European Conference on Computer Vision. Munich， Germany： Springer： 334-349 ［DOI： 10.1007/978-3-030-01261-8_20http://dx.doi.org/10.1007/978-3-030-01261-8_20］
Zhang L D and Peng Z M. 2019. Infrared small target detection based on partial sum of the tensor nuclear norm. Remote Sensing， 11（4）： #382 ［DOI： 10.3390/rs11040382http://dx.doi.org/10.3390/rs11040382］
Zhang M J， Zhang R， Yang Y X， Bai H C， Zhang J and Guo J. 2022. ISNet： shape matters for infrared small target detection//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. New Orleans， USA： IEEE： 867-876 ［DOI： 10.1109/CVPR52688.2022.00095http://dx.doi.org/10.1109/CVPR52688.2022.00095］
Zhang Q， Zhu H T， Cheng H， Zhang J and Zhang Y. 2022. Lightweight infrared small-target detection algorithm. Laser and Optoelectronics Progress， 59（16）： #1611007
章琦， 朱鸿泰， 程虎， 张俊， 张晔. 2022. 轻量级红外弱小目标检测算法. 激光与光电子学进展， 59（16）： #1611007 ［DOI： 10.3788/LOP202259.1611007http://dx.doi.org/10.3788/LOP202259.1611007］
Zhang T F， Li L， Cao S Y， Pu T and Peng Z M. 2023. Attention-guided pyramid context networks for detecting infrared small target under complex background. IEEE Transactions on Aerospace and Electronic Systems， 59（4）： 4250-4261 ［DOI： 10.1109/TAES.2023.3238703http://dx.doi.org/10.1109/TAES.2023.3238703］
Zhang T F， Wu H， Liu Y H， Peng L B， Yang C P and Peng Z M. 2019. Infrared small target detection based on non-convex optimization with Lp-norm constraint. Remote Sensing， 11（5）： #559 ［DOI： 10.3390/rs11050559http://dx.doi.org/10.3390/rs11050559］
Zhang Y X， Li L and Xin Y H. 2019. Infrared small target detection based on adaptive double-layer TDLMS filter. Acta Photonica Sinica， 48（9）： #0910001
张艺璇， 李玲， 辛云宏. 2019. 基于自适应双层TDLMS滤波的红外小目标检测. 光子学报， 48（9）： #0910001 ［DOI： 10.3788/gzxb20194809.0910001http://dx.doi.org/10.3788/gzxb20194809.0910001］
Zhang Z L， Zhang X Y， Peng C， Cheng D Z and Sun J. 2018. ExFuse： enhancing feature fusion for semantic segmentation//Proceedings of the 15th European Conference on Computer Vision. Munich， Germany： Springer： 273-288 ［DOI： 10.1007/978-3-030-01249-6_17http://dx.doi.org/10.1007/978-3-030-01249-6_17］
Zhao B， Wang C P， Fu Q and Han Z S. 2021. A novel pattern for infrared small target detection with generative adversarial network. IEEE Transactions on Geoscience and Remote Sensing， 59（5）： 4481-4492 ［DOI： 10.1109/TGRS.2020.3012981http://dx.doi.org/10.1109/TGRS.2020.3012981］
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.1004-4132.2011.06.004http://dx.doi.org/10.3969/j.issn.1004-4132.2011.06.004］
Zhao M J， Li W， Li L， Hu J， Ma P G and Tao R. 2022. Single-frame infrared small-target detection： a survey. IEEE Geoscience and Remote Sensing Magazine， 10（2）： 87-119 ［DOI： 10.1109/MGRS.2022.3145502http://dx.doi.org/10.1109/MGRS.2022.3145502］
Zhou H， Tian C N， Zhang Z X， Li C Y， Xie Y Q and Li Z B. 2022. PixelGame： infrared small target segmentation as a Nash equilibrium. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 15： 8010-8024 ［DOI： 10.1109/JSTARS.2022.3206062http://dx.doi.org/10.1109/JSTARS.2022.3206062］
Zhu H， Ni H P， Liu S M， Xu G X and Deng L Z. 2020. TNLRS： target-aware non-local low-rank modeling with saliency filtering regularization for infrared small target detection. IEEE Transactions on Image Processing， 29： 9546-9558 ［DOI： 10.1109/TIP.2020.3028457http://dx.doi.org/10.1109/TIP.2020.3028457］
Zuo Z， Tong X Z， Wei J Y， Su S J， Wu P， Guo R Z and Sun B. 2022. AFFPN： attention fusion feature pyramid network for small infrared target detection. Remote Sensing， 14（14）： #3412 ［DOI： 10.3390/rs14143412http://dx.doi.org/10.3390/rs14143412］
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