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orfenet

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

https://github.com/dyl96/orfenet

Science Score: 67.0%

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    Found 2 DOI reference(s) in README
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    Links to: ieee.org
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    Low similarity (12.2%) to scientific vocabulary
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Repository

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

Basic Info
  • Host: GitHub
  • Owner: dyl96
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 19.9 MB
Statistics
  • Stars: 23
  • Watchers: 1
  • Forks: 1
  • Open Issues: 6
  • Releases: 0
Created almost 2 years ago · Last pushed 9 months ago
Metadata Files
Readme License Citation

README.md

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:

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.

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 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}}

Owner

  • Name: 刘东洋
  • Login: dyl96
  • Kind: user
  • Location: 哈尔滨市
  • Company: 哈尔滨工业大学

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "MMDetection Contributors"
title: "OpenMMLab Detection Toolbox and Benchmark"
date-released: 2018-08-22
url: "https://github.com/open-mmlab/mmdetection"
license: Apache-2.0

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Dependencies

docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
mmdet.egg-info/requires.txt pypi
  • asynctest *
  • cityscapesscripts *
  • codecov *
  • cython *
  • flake8 *
  • imagecorruptions *
  • interrogate *
  • isort ==4.3.21
  • kwarray *
  • matplotlib *
  • mmtrack *
  • numpy *
  • onnx ==1.7.0
  • onnxruntime >=1.8.0
  • pycocotools *
  • pytest *
  • scipy *
  • six *
  • sklearn *
  • terminaltables *
  • timm *
  • ubelt *
  • xdoctest >=0.10.0
  • yapf *
requirements/albu.txt pypi
  • albumentations >=0.3.2
requirements/build.txt pypi
  • cython *
  • numpy *
requirements/docs.txt pypi
  • docutils ==0.16.0
  • myst-parser *
  • sphinx ==4.0.2
  • sphinx-copybutton *
  • sphinx_markdown_tables *
  • sphinx_rtd_theme ==0.5.2
requirements/mminstall.txt pypi
  • mmcv-full >=1.3.17
requirements/optional.txt pypi
  • cityscapesscripts *
  • imagecorruptions *
  • scipy *
  • sklearn *
  • timm *
requirements/readthedocs.txt pypi
  • mmcv *
  • torch *
  • torchvision *
requirements/runtime.txt pypi
  • matplotlib *
  • numpy *
  • pycocotools *
  • six *
  • terminaltables *
requirements/tests.txt pypi
  • asynctest * test
  • codecov * test
  • flake8 * test
  • interrogate * test
  • isort ==4.3.21 test
  • kwarray * test
  • onnx ==1.7.0 test
  • onnxruntime >=1.8.0 test
  • pytest * test
  • ubelt * test
  • xdoctest >=0.10.0 test
  • yapf * test
requirements.txt pypi
setup.py pypi