Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (5): 1714-1721.doi: 10.13229/j.cnki.jdxbgxb.20230785

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Infrared small target detection based on cascaded nested U-Net

Ya-li XUE1(),Tong-an YU1,Shan CUI2,Li-zun ZHOU1   

  1. 1.College of Automation Engineering，Nanjing Univercity of Aeronautics and Astronautics，Nanjing 210000，China
    2.Shanghai Electro-Mechanical Engineering Institute，Shanghai 201109，China
  • Received:2023-07-26 Online:2025-05-01 Published:2025-07-18

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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

CLC Number: 

  • TP391

Fig.1

Infrared small target detection block diagram based on CNU-Net"

Fig.2

Feature extraction module"

Fig.3

Convolutional block attention module"

Fig.4

Sliding window"

Fig.5

Contrast feature extraction module"

Fig.6

Detection performance of different layers of U-Net"

Table 1

Ablation experimental results"

实验序号NSCSCFEMPrecisionRecallmIoU
1√√×90.1178.6674.68
2√×√91.1479.6475.19
3√××88.0476.8371.16
4×√×86.4774.8968.94
5√√√93.3681.7878.61

Fig.7

Visual feature map of CFEM input and output"

Fig.8

CNU-Net recognition target effect comparison diagram with or without nested structure"

Table 2

Comparative experimental results"

方法mIoU/%Precision/%Recall/%Inference time
Top-hat9.0578.030.39—
PSTNN32.9973.4835.27—
LCM4.5355.220.45—
MDvsFA63.8386.9668.330.021
ALC-Net74.6989.9480.130.032
CNU-Net78.6193.3681.780.028

Fig.9

ROC curves of different algorithms"

Fig.10

Infrared small target image detection results by different algorithms"

[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.
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