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Journal of Projectiles, Rockets, Missiles and Guidance >

2026 , Vol. 46 >Issue 3: 291 - 304

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2026.03.007

Infrared Small Target Detection Based on Multi-scale Spatial Loss and Morphological Features

  • ZHOU Zhuo 1 ,
  • TAN Shuaibing 2 ,
  • BAI Kun 1 ,
  • XUE Yao 2 ,
  • LU Yifei 3, 4, 5 ,
  • WANG Zheng 3, 4, 5 ,
  • WANG Xiaotian , 3, 4, 5, *
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  • 1 Xi’an Institute of Modern Control Technology， Xi’an 710065，Shaanxi， China
  • 2 Faculty of Electronic and Information Engineering， Xi’an Jiaotong University， Xi’an 710049，Shaanxi， China
  • 3 Unmanned System Research Institute， Northwestern Polytechnical University， Xi’an 710072，Shaanxi， China
  • 4 National Key Laboratory of Unmanned Aerial Vehicle Technology， Northwestern Polytechnical University， Xi’an 710072，Shaanxi， China
  • 5 Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology， Northwestern Polytechnical University， Xi’an 710072，Shaanxi， China

Received date: 2026-02-14

  Online published: 2026-06-29

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Abstract

Infrared small target detection has attracted significant attention due to its strategic value in the key fields such as space-based early warning and maritime rescue.However，the extremely small pixel size，low signal-to-noise ratio，and complex background characteristics make it become a highly challenging visual task.Although the existing deep learning methods significantly outperform the traditional models，the intersection-over-union loss functions commonly used in the existing deep learning methods lack sensitivity to the absolute scale and spatial position variations of predicted targets，resulting in difficulties in achieving pixel-level precise localization and becoming a bottleneck for further performance improvement.To address these issues，this paper proposes an infrared small target detection method based on multi-scale spatial loss and morphological features.Firstly，an absolute-spatial ration （AR） loss function is designed，which enhances the perception of target scaletarget scale by introducing an adaptive dynamic weight based on area differences，and the radial-angular penalty terms in a polar coordinate system are constructed to refine the localization constraints of the center point.Secondly，a lightweight multi-scale prediction head structure is constructed in the U-Net decoder，thereby applying AR loss synchronously to the prediction outputs at different resolution levels to achieve a coarse-to-fine hierarchical supervision.Finally，a dual-stage morphological enhancement strategy for training and testing is constructed，embedding the structural priors via pooling-based differentiable morphological operators during training and correcting the connectivity of the predicted results through opening and closing operations during testing.On the IRSTD1k dataset，the proposed method achieves an intersection-over-union （IoU） of 67.59%，a detection rate （Pd） of 93.02%，and a false alarm rate （Fa） of 9.034×10-6.Compared to the existing mainstream method DNANet，it improves IoU and Pd by 1.88% and 1.18%，respectively，and reduces the false alarm rate by 48.7%.This achieves a better balance between computational efficiency and detection accuracy.

Key words： infrared small target detection; absolute spatial loss; multi-scale structure; morphological feature; deep learning

Cite this article

ZHOU Zhuo , TAN Shuaibing , BAI Kun , XUE Yao , LU Yifei , WANG Zheng , WANG Xiaotian . Infrared Small Target Detection Based on Multi-scale Spatial Loss and Morphological Features[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2026 , 46(3) : 291 -304 . DOI: 10.15892/j.cnki.djzdxb.2026.03.007

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