<p align=center> Exploring Lightweight Structures for Tiny Object Detection in Remote Sensing Images (IEEE TGRS 2025) </p>

1. Required environments:

• Linux
• Python 3.6+
• PyTorch 1.3+
• CUDA 9.2+
• GCC 5+
• MMCV
• cocoapi-aitod

2. Install and start ORFENet:

Note that our ORFENet is based on the MMDetection 2.24.1. Assume that your environment has satisfied the above requirements, please follow the following steps for installation.

git clone https://github.com/dyl96/LTDNet.git
cd LTDNet
pip install -r requirements/build.txt
python setup.py develop

Prepare Dataset:

Download AI-TODv2 dataset; Download LEVIR-Ship dataset.

Train and test：

Train aitodv2 dataset:

python tools/train.py configs_ltdnet/ltdnet/LTDNet_AI_TODv2.py

Train LEVIR-Ship dataset:

python tools/train.py configs_ltdnet/ltdnet/LTDNet_LEVIR_Ship.py

The Pretrained models is on the folder work_dirs.

3. Checkpoint Download（checkpoint-299.pth）：

Baidu Pan：https://pan.baidu.com/s/1eyJiSV12hX6gggiuq8-DFA?pwd=uon2 code：t843

4. Citation

Please cite our paper if you find the work useful:

@ARTICLE{10988878,
  author={Liu, Dongyang and Zhang, Junping and Qi, Yunxiao and Xi, Yunqiao and Jin, Jing},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Exploring Lightweight Structures for Tiny Object Detection in Remote Sensing Images}, 
  year={2025},
  volume={63},
  number={},
  pages={1-15},
  doi={10.1109/TGRS.2025.3567345}}

@ARTICLE{10462223,
  author={Liu, Dongyang and Zhang, Junping and Qi, Yunxiao and Wu, Yinhu and Zhang, Ye},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Tiny Object Detection in Remote Sensing Images Based on Object Reconstruction and Multiple Receptive Field Adaptive Feature Enhancement}, 
  year={2024},
  volume={62},
  number={},
  pages={1-13},
  doi={10.1109/TGRS.2024.3381774}}

@ARTICLE{10462223,
  author={Liu, Dongyang and Zhang, Junping and Qi, Yunxiao and Wu, Yinhu and Zhang, Ye},
  journal={IEEE Geoscience and Remote Sensing Letters}, 
  title={A Tiny Object Detection Method Based on Explicit Semantic Guidance for Remote Sensing Images}, 
  year={2024},
  volume={21},
  number={},
  pages={1-5},
  doi={10.1109/LGRS.2024.3374418}}