基于级联嵌套U-Net的红外小目标检测

doi:10.13229/j.cnki.jdxbgxb.20230785

摘要/Abstract

摘要：

针对复杂场景下红外小目标尺寸差异大、检测效果欠佳的问题，提出了一种基于级联嵌套U-Net的红外小目标检测方法。首先，为解决不同场景下小目标尺寸差异大的问题，搭建了3种深度的U-Net网络，并将3个U-Net网络以级联嵌套的方式组成检测模型；其次，引入对比度信息抽取模块，以进一步丰富特征信息，抑制密集背景噪声的干扰；最后，将所提出的算法与5种主流的算法进行比较。实验结果表明：本文算法性能优于其他算法，并且平均交并比、精确率和召回率分别达到了78.61%、93.36%和81.78%。

关键词: 信息处理技术, 红外小目标, 对比度信息, 注意力机制, 检测

Abstract:

Aiming at the problem of large size differences in infrared small targets and poor detection results in complex scenes， an infrared small target detection method based on cascaded nested U-Net is proposed. First， in order to solve the problem of large size differences between small targets in different scenarios， three depths of U-Net networks were built， and the three U-Net networks were cascaded and nested to form a detection model； secondly， contrast was used information extraction module to further enrich feature information and suppress the interference of dense background noise； finally， the proposed algorithm is compared with five mainstream algorithms. The experimental results show that the performance of this algorithm is better than other algorithms， and the average intersection and union ratio， the precision rate and recall rate reached 78.61%， 93.36% and 81.78% respectively.

Key words: information processing technology, infrared small target, contrast information, attention mechanism, detection

中图分类号:

TP391

薛雅丽,俞潼安,崔闪,周李尊. 基于级联嵌套U-Net的红外小目标检测[J]. 吉林大学学报(工学版), 2025, 55(5): 1714-1721.

Ya-li XUE,Tong-an YU,Shan CUI,Li-zun ZHOU. Infrared small target detection based on cascaded nested U-Net[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(5): 1714-1721.

图/表 12

图1

图2

图3

图4

图5

图6

表1

图7

图8

表2

图9

图10

参考文献 17

[1]	查月, 汤晔. 红外海面小目标检测算法综述[J]. 宇航计测技术, 2022, 42(6): 57-65.
	Zha Yue, Tang Ye. Review of infrared sea surface small target detection algorithm [J]. Journal of Astronautic Metrology and Measurement, 2022, 42(6): 57-65.
[2]	Rivest J F, Fortin R. Detection of dim targets in digital infrared imagery by morphological image processing[J]. Optical Engineering, 1996, 35(7): 1886-1893.
[3]	Chen C L P, Li H, Wei Y, et al. A local contrast method for small infrared target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 52(1): 574-581.
[4]	张文杰, 熊庆宇. 基于对比度与空间位置特征的显著性区域检测[J]. 吉林大学学报: 工学版, 2015, 45(5): 1709-1716.
	Zhang Wen-jie, Xiong Qing-yu. Joint segmentation of optic cup and disc based on high resolution network [J]. Journal of Jilin University (Engineering and Technology Edition), 2015, 45(5): 1709-1716.
[5]	Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. Advances in Neural Information Processing Systems, 2015, 28: 7485869.
[6]	Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]∥ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 779-788.
[7]	Wang H, Zhou L, Wang L. Miss detection vs false alarm: adversarial learning for small object segmentation in infrared images[C]∥Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), 2019: 8509-8518.
[8]	Deng C, Wang M, Liu L, et al. Extended feature pyramid network for small object detection[J]. IEEE Transactions on Multimedia, 2021, 24: 1968-1979.
[9]	Goodfellow I, Pouget A J, Mirza M, et al. Generative adversarial nets[J]. Advances in Neural Information Processing Systems, 2014, 2: 2672-2680.
[10]	Wu X, Hong D, Chanussot J. UIU-Net: U-Net in U-Net for infrared small object detection[J]. IEEE Transactions on Image Processing, 2022, 32: 364-376.
[11]	Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[C]// Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, 2015: 234-241.
[12]	Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 2117-2125.
[13]	Woo S, Park J, Lee J Y, et al. Cbam: convolutional block attention module[C]∥Proceedings of the European Conference on Computer Vision, Munich, Germany, 2018: 3-19.
[14]	Dai Y, Wu Y, Zhou F, et al. Asymmetric contextual modulation for infrared small target detection[C]∥Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, USA, 2021: 950-959.
[15]	Li B, Xiao C, Wang L, et al. Dense nested attention network for infrared small target detection[J]. IEEE Transactions on Image Processing, 2022, 32: 1745-1758.
[16]	Dai Y, Wu Y, 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.
[17]	Sun Y, Yang J, 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, 2020, 59(5): 3737-3752.

相关文章 15

[1]	田丽,贾煜辉. 改进YOLOv5s算法的高光谱遥感图像目标检测[J]. 吉林大学学报(工学版), 2025, 55(5): 1742-1748.
[2]	侯越,郭劲松,林伟,张迪,武月,张鑫. 分割可跨越车道分界线的多视角视频车速提取方法[J]. 吉林大学学报(工学版), 2025, 55(5): 1692-1704.
[3]	张汝波,常世淇,张天一. 基于深度学习的图像信息隐藏方法综述[J]. 吉林大学学报(工学版), 2025, 55(5): 1497-1515.
[4]	王军,司昌馥,王凯鹏,付强. 融合集成学习技术和PSO-GA算法的特征提取技术的入侵检测方法[J]. 吉林大学学报(工学版), 2025, 55(4): 1396-1405.
[5]	张彬桥,吴健. 基于跟踪-学习-检测算法的四旋翼无人机自主降落姿态控制方法[J]. 吉林大学学报(工学版), 2025, 55(4): 1419-1425.
[6]	聂为之,尹斐,苏毅珊. 任务驱动下成像声呐水下目标识别方法综述[J]. 吉林大学学报(工学版), 2025, 55(4): 1163-1175.
[7]	才华,王玉瑶,付强,马智勇,王伟刚,张晨洁. 基于注意力机制和特征融合的语义分割网络[J]. 吉林大学学报(工学版), 2025, 55(4): 1384-1395.
[8]	郭宁,胡小晨,董德存. 基于改进YOLO算法的地铁车厢客流检测方法[J]. 吉林大学学报(工学版), 2025, 55(4): 1258-1265.
[9]	程光,李沛霖. 基于MSE改进BiLSTM网络算法的工业互联网异常流量时空融合检测[J]. 吉林大学学报(工学版), 2025, 55(4): 1406-1411.
[10]	张河山,范梦伟,谭鑫,郑展骥,寇立明,徐进. 基于改进YOLOX的无人机航拍图像密集小目标车辆检测[J]. 吉林大学学报(工学版), 2025, 55(4): 1307-1318.
[11]	孙淑琴,祁鑫,袁正海,李再华,唐晓骏. 基于耦合混沌系统阵列的电力系统微弱谐波检测方法[J]. 吉林大学学报(工学版), 2025, 55(3): 1082-1092.
[12]	李学军,权林霏,刘冬梅,于树友. 基于Faster-RCNN改进的交通标志检测算法[J]. 吉林大学学报(工学版), 2025, 55(3): 938-946.
[13]	李扬,李现国,苗长云,徐晟. 基于双分支通道先验和Retinex的低照度图像增强算法[J]. 吉林大学学报(工学版), 2025, 55(3): 1028-1036.
[14]	张兰芳,李根泽,刘婷宇,余博. 局部多车影响下跟驰行为机理及建模[J]. 吉林大学学报(工学版), 2025, 55(3): 963-973.
[15]	单泽彪,薛泓垚,刘小松,姚瑞广,陈广秋. Alpha稳定分布噪声下基于近似l₀范数稀疏重构的波达方向估计[J]. 吉林大学学报(工学版), 2025, 55(3): 1093-1102.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

实验序号	NS	CS	CFEM	Precision	Recall	mIoU
1	√	√	×	90.11	78.66	74.68
2	√	×	√	91.14	79.64	75.19
3	√	×	×	88.04	76.83	71.16
4	×	√	×	86.47	74.89	68.94
5	√	√	√	93.36	81.78	78.61

方法	mIoU/%	Precision/%	Recall/%	Inference time
Top-hat	9.05	78.03	0.39	—
PSTNN	32.99	73.48	35.27	—
LCM	4.53	55.22	0.45	—
MDvsFA	63.83	86.96	68.33	0.021
ALC-Net	74.69	89.94	80.13	0.032
CNU-Net	78.61	93.36	81.78	0.028