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DiffDet4SAR

DiffDet4SAR: Diffusion-based Aircraft Target Detection Network for SAR Images (GRSL 2024)

Install / Use

/learn @JoyeZLearning/DiffDet4SAR
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

DiffDet4SAR: Diffusion-based Aircraft Target Detection Network for SAR Images

👑DiffDet4SAR is the first work of diffusion model for SAR image aircraft target detection.

DiffDet4SAR: Diffusion-based Aircraft Target Detection Network for SAR Images
published by GRSL DOI: 10.1109/LGRS.2024.3386020

🛠️ Updates

  • (04/2024) Code is released.

🕸️ Dataset

SAR-AIRcraft1.0 (doi: 10.12000/JR23043)

📽️ Getting Started

The installation instruction and usage are in Getting Started with DiffusionDet.

🚉 Train/Evalution:

  1. modifying the weight in DiffusionDet-main/configs/Base-DiffusionDet.yaml (use pre-train res50)
  2. modifying the weight in DiffusionDet-main/configs/diffdet.coco.res50.300boxes.yaml
  3. modifying DiffusionDet-main/detectron2/engine/defaults.py and the 98-122 line to your root.
  4. As for other configs and their meaning, DifffusionDet is introduced in detail.
  5. ATTENTION:In order to use the code directly and reduce the complexity of the code, I changed the images and annotations of the aircraft dataset into the coco format, and put them in the folder named coco.
  6. image

🏝️ Quantative Results:

Quantitative results of different models evaluated by AP@50.

*The overall repository style is highly borrowed from DifffusionDet. Thanks to Shoufa Chen.

License

This project is under the CC-BY-NC 4.0 license. See LICENSE for details.

🍭 GOOD NEW!!!

DiffDet4SAR has entered the top 1% of ESI highly cited papers and obtained her life trophy on May, 08, 2025 🎉🎉🎉 8ace4a5b0d061265d1f6c869093c0e0c

💡 Citing DiffDet4SAR

If you find DiffDet4SAR helpful to your research or wish to refer to the baseline results published here, please use the following BibTeX entry.

@ARTICLE{10494361,
  author={Zhou, Jie and Xiao, Chao and Peng, Bo and Liu, Zhen and Liu, Li and Liu, Yongxiang and Li, Xiang},
  journal={IEEE Geoscience and Remote Sensing Letters}, 
  title={DiffDet4SAR: Diffusion-Based Aircraft Target Detection Network for SAR Images}, 
  year={2024},
  volume={21},
  number={},
  pages={1-5},
  keywords={Aircraft;Object detection;Radar polarimetry;Feature extraction;Scattering;Noise;Convolution;Aircraft target detection;diffusion model;synthetic aperture radar (SAR)},
  doi={10.1109/LGRS.2024.3386020}}

Please light up the STAR⭐⭐⭐⭐⭐ to encourage more opensource on SAR image interpretations! 🥰🥳🥂

View on GitHub
GitHub Stars90
CategoryDevelopment
Updated4d ago
Forks11

Languages

Python

Security Score

80/100

Audited on Mar 31, 2026

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