Tiny Object Detection in Remote Sensing Images Based on Object Reconstruction and Multiple Receptive Field Adaptive Feature Enhancement (IEEE TGRS 2024)

This repository contains python implementation of our paper ORFENet.

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/ORFENet.git
cd ORFENet
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_orfenet/orfenet/aitodv2_fcos_r50_p2_hrfe_or_3x.py

Train LEVIR-Ship dataset:

python tools/train.py configs_orfenet/orfenet/levir_ship_fcos_r50_p2_hrfe_or_1x.py

Test LEVIR-Ship dataset:

python tools/test.py configs_orfenet/orfenet/levir_ship_fcos_r50_p2_hrfe_or_1x.py work_dirs/levir_ship_fcos_r50_p2_hrfe_or_1x/epoch_12.pth --eval bbox

Checkpoint Download：

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

3. Visual Results

Visual comparisons of the proposed method and other methods. (a) the baseline FCOS. (b) FSANet. (c) Cascade-R-CNN w/ NWD-RKA. (d) The proposed ORFENet. The green boxes denote the true positive predictions, the red boxes denote the false negative predictions, and the blue boxes denote the false positive predictions.

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}}

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
assets		assets
configs		configs
configs_orfenet		configs_orfenet
demo		demo
docker		docker
docs		docs
mmdet.egg-info		mmdet.egg-info
mmdet		mmdet
requirements		requirements
resources		resources
tests		tests
tools		tools
work_dirs		work_dirs
.gitattributes		.gitattributes
CITATION.cff		CITATION.cff
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
model-index.yml		model-index.yml
pytest.ini		pytest.ini
requirements.txt		requirements.txt
setup.cfg		setup.cfg
setup.py		setup.py

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Tiny Object Detection in Remote Sensing Images Based on Object Reconstruction and Multiple Receptive Field Adaptive Feature Enhancement (IEEE TGRS 2024)

1. Required environments:

2. Install and start ORFENet:

Prepare Dataset:

Train and test：

Train aitodv2 dataset:

Train LEVIR-Ship dataset:

Test LEVIR-Ship dataset:

Checkpoint Download：

3. Visual Results

4. Citation

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Tiny Object Detection in Remote Sensing Images Based on Object Reconstruction and Multiple Receptive Field Adaptive Feature Enhancement (IEEE TGRS 2024)

1. Required environments:

2. Install and start ORFENet:

Prepare Dataset:

Train and test：

Train aitodv2 dataset:

Train LEVIR-Ship dataset:

Test LEVIR-Ship dataset:

Checkpoint Download：

3. Visual Results

4. Citation

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