1. This code integrates a series of infrared small target detection networks we built: MLCL-Net [paper], ALCL-Net [paper], GGL-Net [paper] and MSDA-Net [Paper/arXiv]. For details, please see the "model" folder.

2. It is worth mentioning that our solution based on MSDA-Net won the first prize in the PRCV2024 Wide-area Infrared Small Target Detection Challenge

3. Everyone is welcome to follow our new work [PAL Framework] on the task of "Infrared small target detection with single point supervision". On the task of "Infrared small target detection with single point supervision", equipped with our proposed "PAL Framework", MSDA-Net also has SOTA performance.

4. !!! We strongly recommend using our integrated framework [PAL Framework], which incorporates multiple networks, including MSDA-Net, and allows convenient switching between different supervision paradigms (Full supervision and Single point supervision) as well as multiple datasets (SIRST, NUDT-SIRST, IRSTD-1k and SIRST3). In addition, compared with this released codebase that resizes training images to a fixed resolution, the PAL framework adopts a cropping strategy with a patch size of 256×256 pixels. Extensive empirical evaluations demonstrate that the PAL framework provides a more reasonable basic training framework and consistently achieves superior performance.

MSDA-Net

1. Original datasets

• NUDT-SIRST [Original dataset] [paper]
• SIRST [Original dataset] [paper]
• IRSTD-1k [Original dataset] [paper]

2. The datasets we created from original datasets (can be used directly in our demo)

• 💎 Download the dataset required by our code!!!

1. Download the dataset

        Click download datasets

        Unzip the downloaded compressed package to the root directory of the project.

2. Creat a Anaconda Virtual Environment

   conda create -n MSDA-Net python=3.8 
   activate MSDA-Net 
   

3. Configure the running environment

    pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
    pip install segmentation_models_pytorch -i https://pypi.tuna.tsinghua.edu.cn/simple
    pip install PyWavelets -i https://pypi.tuna.tsinghua.edu.cn/simple
    pip install scikit-image -i https://pypi.tuna.tsinghua.edu.cn/simple
    pip install albumentations==1.3.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
    pip install scikit-learn matplotlib thop h5py SimpleITK scikit-image medpy yacs torchinfo
   

4. Training the model

   The default dataset is IRSTD-1K. If you need to train others, please modify the corresponding dataset path.
   (If you want to train NUDT-SIRST, please note that the parameter IMAGE_SIZE in the training file needs to be changed to 256.)

   python train_MSDA_Net.py
   

5. Testing the Model

   Click download weight files in the paper (Unzip the downloaded package to the root directory of the project)

   The default dataset is IRSTD-1K. If you need to test the weight file under other datasets, please modify the corresponding dataset path and weight file path.
   (If you want to train NUDT-SIRST, please note that the parameter IMAGE_SIZE in the training file needs to be changed to 256.)

   python test_MSDA_Net.py
   

• Quantative Results on NUDT-SIRST:

Comparison of MSDA-Net and various SOTA methods on the NUDT-SIRST dataset

• Quantative Results on SIRST:

Comparison of MSDA-Net and various SOTA methods on the SIRST dataset

• Quantative Results on IRSTD-1k:

Comparison of MSDA-Net and various SOTA methods on the IRSTD-1k dataset

• MSDA-Net weight files for each dataset:

  Dataset	miou	niou	Pd	Fa(×10-6)	weight
  NUDT-SIRST (1 : 1)	0.938	0.941	0.992	3.70	MSDA_Net_nudt_1_1.pth
  NUDT-SIRST (10 : 3)	0.950	0.951	0.995	3.66	MSDA_Net_nudt_3_7.pth
  SIRST (341 : 86)	0.811	0.794	1.0	7.19	MSDA_Net_SIRST_341_86.pth
  SIRST (224 : 96)	0.800	0.775	1.0	5.01	MSDA_Net_SIRST_224_96.pth
  IRSTD-1k	0.719	0.692	0.943	11.39	MSDA_Net_IRSTD_1K.pth

If you find this repo helpful, please give us a 🤩star🤩. Please consider citing the MSDA-Net if it benefits your project.

BibTeX reference is as follows.

@misc{zhao2024multiscaledirectionawarenetworkinfrared,
      title={Multi-Scale Direction-Aware Network for Infrared Small Target Detection}, 
      author={Jinmiao Zhao and Zelin Shi and Chuang Yu and Yunpeng Liu},
      year={2024},
      eprint={2406.02037},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2406.02037}, 
}

word reference is as follows.

Jinmiao Zhao, Zelin Shi, Chuang Yu, and Yunpeng Liu. Multi-Scale Direction-Aware Network for Infrared Small Target Detection. arXiv preprint arXiv:2406.02037, 2024.

1. My homepage: [YuChuang]
2. My "PAL Framework" project code (ICCV2025)：[Link]
3. My latest "FDEP Framework" project code: [Link]