SP-KAN: Sparse-sine Perception Kolmogorov–Arnold Networks for Infrared Small Target Detection [Paper] [Weight]

Shuai Yuan, Yu Liu, Xiaopei Zhang, Xiang Yan, Hanlin Qin, Naveed Akhtar, ISPRS Journal of Photogrammetry and Remote Sensing 2026.

If the implementation of this repo is helpful to you, just star it！ ⭐⭐⭐

Structure

Introduction

We present a Sparse-sine Perception Kolmogorov–Arnold Networks (SP-KAN) to the IRSTD task. Experiments on public datasets demonstrate the effectiveness of our method. Our main contributions are as follows:

1. We propose SP-KAN, which reformulate IRSTD as a global context modulation problem driven by sparse nonlinear modules.

2. We design a pattern complementarity module (PCM) to capture unstructured dependencies and local saliency interactions, enhancing target–background separability.

3. We devise a sparse-sine perception Kolmogorov–Arnold layer (SPKAL) to unlock the nonlinear representational potential of the KAN layer.

Usage

1. Data

The SIRST3 dataset, which combines IRSTD-1K, NUDT-SIRST, and SIRST-v1, is used to train SCTransNet.

• SIRST-v1   [download]   [paper]

• NUDT-SIRST   [download]   [paper]

• IRSTD-1K   [download dir]   [paper]

• Our project has the following structure:

  ├──./datasets/
  │    ├── IRSTD-1K
  │    │    ├── images
  │    │    │    ├── XDU0.png
  │    │    │    ├── XDU1.png
  │    │    │    ├── ...
  │    │    ├── masks
  │    │    │    ├── XDU0.png
  │    │    │    ├── XDU1.png
  │    │    │    ├── ...
  │    │    ├── img_idx
  │    │    │    ├── train_IRSTD-1K.txt
  │    │    │    ├── test_IRSTD-1K.txt
  │    ├── NUDT-SIRST
  │    │    ├── images
  │    │    │    ├── 000001.png
  │    │    │    ├── 000002.png
  │    │    │    ├── ...
  │    │    ├── masks
  │    │    │    ├── 000001.png
  │    │    │    ├── 000002.png
  │    │    │    ├── ...
  │    │    ├── img_idx
  │    │    │    ├── train_NUDT-SIRST.txt
  │    │    │    ├── test_NUDT-SIRST.txt
  │    ├── SIRSTv1 (~which is misnamed as NUAA-SIRST~)
  │    │    ├── images
  │    │    │    ├── Misc_1.png
  │    │    │    ├── Misc_2.png
  │    │    │    ├── ...
  │    │    ├── masks
  │    │    │    ├── Misc_1.png
  │    │    │    ├── Misc_2.png
  │    │    │    ├── ...
  │    │    ├── img_idx
  │    │    │    ├── train_NUAA-SIRST.txt
  │    │    │    ├── test_NUAA-SIRST.txt
  │    ├── SIRST3 (~The sum of SIRSTv1, NUDT-SIRST and IRSTD-1K~)
  │    │    ├── images
  │    │    │    ├── XDU0.png
  │    │    │    ├── XDU1.png
  │    │    │    ├── ...
  │    │    ├── masks
  │    │    │    ├── XDU0.png
  │    │    │    ├── XDU1.png
  │    │    │    ├── ...
  │    │    ├── img_idx
  │    │    │    ├── train_SIRST3.txt
  │    │    │    ├── test_SIRST3.txt
  
  

2. Train.

python train.py

3. Test and demo.

权重文件的百度网盘链接：https://pan.baidu.com/s/1lzdUAQpw4oLSktdxbd-tRQ?pwd=6666

权重文件的谷歌云盘链接：https://drive.google.com/file/d/1LF0TRmxJ0J8zm8IiDG37LH_7_9dLuFVp/view?usp=sharing

python test.py

Results and Trained Models

Qualitative Results

Quantitative Results on SIRSTv1, NUDT-SIRST, IRSTD-1K， and SIRST3. i.e, one weight for four Datasets.

Model	mIoU (x10(-2))	F-measure (x10(-2))	Pd (x10(-2))	Fa (x10(-6))
SIRST v1	79.15	88.36	97.72	14.82
NUDT-SIRST	95.24	97.56	99.36	3.17
IRSTD-1K	68.02	80.96	93.27	10.12
SIRST3	84.20	91.42	98.14	8.01

*This code is highly borrowed from IRSTD-Toolbox. Thanks to Xinyi Ying.

*The overall repository style is highly borrowed from DNA-Net. Thanks to Boyang Li.

Citation

If you find the code useful, please consider citing our paper using the following BibTeX entry.

@article{SP-KAN,
title = {SP-KAN: Sparse-sine perception Kolmogorov–Arnold networks for infrared small target detection},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {234},
pages = {1-19},
year = {2026},
issn = {0924-2716},
doi = {https://doi.org/10.1016/j.isprsjprs.2026.02.019},
url = {https://www.sciencedirect.com/science/article/pii/S0924271626000705},
author = {Shuai Yuan and Yu Liu and Xiaopei Zhang and Xiang Yan and Hanlin Qin and Naveed Akhtar},
}

Contact

Welcome to raise issues or email to yuansy@stu.xidian.edu.cn for any question regarding our SP-KAN.