Awesome

TIOE-Det

This project hosts the official implementation for the paper:

Task Interleaving and Orientation Estimation for High-Precision Oriented Object Detection in Aerial Images [URL][PDF][BibTex]

( accepted by ISPRS Journal of Photogrammetry and Remote Sensing).

Abstract

In this paper, we propose a Task Interleaving and Orientation Estimation Detector for high-quality oriented object detection in aerial images. Specifically, a posterior hierarchical alignment (PHA) indicator is proposed to optimize the detection pipeline. TIOE-Det adopts PHA indicator to integrate fine-grained posterior localization guidance into classification task to address the misalignment between classification and localization subtasks. Then, a balanced alignment loss is developed to solve the imbalance localization loss contribution in PHA prediction. Moreover, we propose a progressive orientation estimation (POE) strategy to approximate the orientation of objects with n-ary codes. On this basis, an angular deviation weighting strategy is proposed to achieve accurate evaluation of angle deviation in POE strategy.

Framework

framework

Setup

conda create -n tioe python=3.6 -y
source activate tioe
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

sudo apt-get install swig
pip install -r requirements.txt

cd  DOTA_devkit
swig -c++ -python polyiou.i
python setup.py build_ext --inplace
cd ..

sh compile.sh

Training

Creat config files.
Dataset transformation via running sh prepare.sh.
Run sh train.sh.

Inference & Testing

Run sh demo.sh and sh test.sh.

Visualizations

demo

Citation

If you find our work or code useful in your research, please consider citing:

@article{MING2023241,
title={Task interleaving and orientation estimation for high-precision oriented object detection in aerial images},
author={Qi Ming and Lingjuan Miao and Zhiqiang Zhou and Junjie Song and Yunpeng Dong and Xue Yang},
journal={ISPRS Journal of Photogrammetry and Remote Sensing},
volume={196},
pages={241-255},
year={2023},
issn = {0924-2716},
doi = {https://doi.org/10.1016/j.isprsjprs.2023.01.001},
}

Feel free to contact me if there are any questions.