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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)

ORFENet 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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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)

This repository contains python implementation of our paper [ORFENet](https://ieeexplore.ieee.org/document/10462223).

### 1. Required environments:
* Linux
* Python 3.6+
* PyTorch 1.3+
* CUDA 9.2+
* GCC 5+
* [MMCV](https://mmcv.readthedocs.io/en/latest/#installation)
* [cocoapi-aitod](https://github.com/jwwangchn/cocoapi-aitod)

### 2. Install and start ORFENet:

Note that our ORFENet is based on the [MMDetection 2.24.1](https://github.com/open-mmlab/mmdetection). Assume that your environment has satisfied the above requirements, please follow the following steps for installation.

```shell script
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](https://drive.google.com/drive/folders/1Er14atDO1cBraBD4DSFODZV1x7NHO_PY?usp=sharing) dataset; Download [LEVIR-Ship](https://github.com/WindVChen/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

 <img src="assets/1.png"/> 

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}}

@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}}

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