石剑,韩晶,滕尚志,吕学强

• 1:北京信息科技大学网络文化与数字传播北京市重点实验室

摘要(Abstract)：

针对复杂背景下红外小目标检测面临的目标像素占比极低、特征表示微弱、背景干扰强等挑战，以及现有深度学习方法全局建模能力不足的局限，提出一种基于频域注意力与边缘增强的检测方法。通过分层双域学习模块（hierarchical dual-domain learning module, HDLM）学习频域与空间域特征，并利用注意力机制对2类特征进行动态加权，以增强网络对小目标的定位精度与抗干扰能力；同时设计边缘增强模块（edge enhancement module, EEM），用于细化小目标边缘特征，提升网络对目标边缘的感知能力。在公共数据集IRSTD-1k上的实验结果显示，相较于当前性能领先的MSHNet(multi-scale head to the plain U-Net)，所提方法的交并比（intersection over union, IoU）提升2.15百分点、检测概率（probability of detection, Pd）提升1.19百分点、虚警率（false alarm rate, Fa）降低5.88×10~(-6)，验证了该方法的优越性。

关键词(KeyWords)： 红外小目标检测;频域特征提取;多尺度特征融合;边缘增强;双域学习

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目（62202061）;; 北京市自然科学基金项目（4232025,4254096）;; 北京市教委科研计划科技一般项目（KM202311232002）

作者(Author): 石剑,韩晶,滕尚志,吕学强

DOI: 10.16508/j.cnki.11-5866/n.2026.02.006

参考文献(References)：

• [1]LIU M,DU H Y,ZHAO Y J,et al. Image small target detection based on deep learning with SNR controlled sample generation[C]//Current Trends in Computer Science and Mechanical Automation. Warsaw, Poland:De Gruyter Open Ltd, 2017, 1:211-220.
• [2]WANG H,ZHOU L P,WANG L. Miss detection vs. false alarm:adversarial learning for small object segmentation in infrared images[C]//2019 IEEE/CVF International Conference on Computer Vision(ICCV). New York, NY, USA:IEEE,2019:8508-8517.
• [3]DAI Y M,WU Y Q,ZHOU F,et al. Asymmetric contextual modulation for infrared small target detection[C]//2021 IEEE Winter Conference on Applications of Computer Vision(WACV). Los Alamitos, CA, USA:IEEE Computer Society,2021:949-958.
• [4]ZHANG M J, WANG Y C,GUO J,et al. IRSAM:advancing segment anything model for infrared small target detection[C]//Computer Vision-ECCV. Cham:Springer,2024:233-249.
• [5]CHI L,JIANG B R,MU Y. Fast Fourier convolution[J].Advances in Neural Information Processing Systems,2020,33:4479-4488.
• [6]DESHPANDE S D,ER M H,VENKATESWARLU R,et al.Max-mean and max-median filters for detection of small targets[J]. Signal and Data Processing of Small Targets,1999,3809:74-85.
• [7]HAN J H,MORADI S,FARAMARZI I,et al. A local contrast method for infrared small-target detection utilizing a tri-layer window[J]. IEEE Geoscience and Remote Sensing Letters,2020,17(10):1822-1826.
• [8]GAO C Q,MENG D Y,YANG Y,et al. Infrared patch-image model for small target detection in a single image[J]. IEEE Transactions on Image Processing,2013,22(12):4996-5009.
• [9]TOM V T,PELI T,LEUNG M,et al. Morphology-based algorithm for point target detection in infrared backgrounds[C]//Signal and Data Processing of Small Targets 1993,Orlando:[s.n],1993:2-11.
• [10]BAI X Z,ZHOU F G. Analysis of new top-hat transformation and the application for infrared dim small target detection[J].Pattern Recognition,2010,43(6):2145-2156.
• [11]习腾彦，袁丽华，王树鹏.基于局部对比度的自适应Top-Hat红外小目标检测[J].激光与光电子学进展，2023,60(16):1628003.XI T Y,YUAN L H,WANG S P. Adaptive Top-Hat infrared small target detection based on local contrast[J]. Laser&Optoelectronics Progress,2023,60(16):1628003.(in Chinese)
• [12]WANG K W,DU S Y,LIU C X,et al. Interior attention-aware network for infrared small target detection[J]. IEEE Transactions on Geoscience and Remote Sensing,2022,60:5002013.
• [13]LI B Y,XIAO C,WANG L G,et al. Dense nested attention network for infrared small target detection[J]. IEEE Transactions on Image Processing,2022,32:1745-1758.
• [14]ZHANG M J,ZHANG R,YANG Y X,et al. ISNet:shape matters for infrared small target detection[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Los Alamitos, CA, USA:IEEE Computer Society,2022:867-876.
• [15]CHEN G,WANG W H,TAN S R. IRSTFormer:a hierarchical vision Transformer for infrared small target detection[J]. Remote Sensing,2022,14(14):3258.
• [16]FAN L Y,WANG Y Y,HU G L,et al. Diffusion-based continuous feature representation for infrared small-dim target detection[J]. IEEE Transactions on Geoscience and Remote Sensing,2024,62:1-17.
• [17]YANG J N,LIU S L,WU J J,et al. Pinwheel-shaped convolution and scale-based dynamic loss for infrared small target detection[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto, CA, USA:AAAI, 2025,39(9):9202-9209.
• [18]WU S L,XIAO C,WANG Y Q,et al. Sparsity-aware global channel pruning for infrared small-target detection networks[J].IEEE Transactions on Geoscience and Remote Sensing,2025,63:5615011.
• [19]LI Q,ZHANG W,LU W X,et al. Multibranch mutual-guiding learning for infrared small target detection[J]. IEEE Transactions on Geoscience and Remote Sensing,2025,63:5605710.
• [20]RONNEBERGER O, FISCHER P, BROX T. U-Net:convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted InterventionMICCAI 2015. Berlin, Germany:Springer-Verlag Berlin,2015, 9351:234-241.
• [21]LI H C,XIONG P F,AN J,et al. Pyramid attention network for semantic segmentation[EB/OL].(2018-11-25)[2025-08-24].https：//arxiv. org/abs/1805. 10180.
• [22]ZHAO M X,CHENG L,YANG X,et al. TBC-Net:a real-time detector for infrared small target detection using semantic constraint[EB/OL].(2019-12-27)[2025-08-24]. https：//arxiv.org/abs/2001. 05852.
• [23]QIN Z Q,ZHANG P Y,WU F,et al. FCANet:frequency channel attention networks[C]//2021 IEEE/CVF International Conference on Computer Vision(ICCV). New York, NY,USA:IEEE, 2021:763-772.
• [24]YUN G,YOO J H,KIM K,et al. SPANet:frequency-balancing token mixer using spectral pooling aggregation modulation[C]//2023 IEEE/CVF International Conference on Computer Vision(ICCV). Los Alamitos, CA, USA:IEEE Computer Society,2023:6090-6101.
• [25]WANG S X,VELDHUIS R,BRUNE C,et al. What do neural networks learn in image classification? A frequency shortcut perspective[C]//2023 IEEE/CVF International Conference on Computer Vision(ICCV). Los Alamitos, CA, USA:IEEE Computer Society, 2023:1433-1442.
• [26]HAN J H,MORADI S,FARAMARZI I,et al. Infrared small target detection based on the weighted strengthened local contrast measure[J]. IEEE Geoscience and Remote Sensing Letters,2021,18(9):1670-1674.
• [27]ZHANG L D,PENG L B,ZHANG T F,et al. Infrared small target detection via non-convex rank approximation minimization joint l2,1 norm[J]. Remote Sensing,2018,10(11):1821.
• [28]DAI Y M,WU Y Q. Reweighted infrared patch-tensor model with both nonlocal and local priors for single-frame small target detection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2017,10(8):3752-3767.
• [29]ZHANG L D,PENG Z M. Infrared small target detection based on partial sum of the tensor nuclear norm[J]. Remote Sensing,2019,11(4):382.
• [30]SUN Y,YANG J G,AN W. Infrared dim and small target detection via multiple subspace learning and spatial-temporal patch-tensor model[J]. IEEE Transactions on Geoscience and Remote Sensing,2021,59(5):3737-3752.
• [31]DAI Y M,WU Y Q,ZHOU F,et al. Attentional local contrast networks for infrared small target detection[J]. IEEE Transactions on Geoscience and Remote Sensing,2021,59(11):9813-9824.
• [32]SUN H,BAI J X,YANG F,et al. Receptive-field and direction induced attention network for infrared dim small target detection with a large-scale dataset IRDST[J]. IEEE Transactions on Geoscience and Remote Sensing,2023,61:5000513.
• [33]LIU Q K,LIU R,ZHENG B L, et al. Infrared small target detection with scale and location sensitivity[C]//2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Los Alamitos, CA, USA:IEEE Computer Society,2024:17490-17499.