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Align Deep Features for Oriented Object Detection,
Jiaming Han^*, Jian Ding^*, Jie Li, Gui-Song Xia^†,
arXiv preprint (arXiv:2008.09397) / TGRS (IEEE Xplore).

The repo is based on mmdetection, S2ANet branch pytorch1.9, and UCAS-AOD-benchmark thanks to their work.

Two versions are provided here: Original version and v20210104. We recommend to use v20210104 (i.e. the master branch).

As there is a need for me to run S2ANet on UCAS_AOD. However, there is no present work to do this. This repo is both a tutorial and an extension to original project S2ANet. Besides, I used UCAS-AOD-benchmark to prepare for dataset.

The main problems this repo solved are:

• custom dataset training(UCAS_AOD as an example)
• change the backbone to ResNeXt101x64_4d to gain more performance.(this pretrain model is provided in the link below, after downloading, move it to torch pretrain cache dir)
• a tutorial for begineers in remote-sensing
• provide some pretrained models with baidu Netdisk
• align the accuracy provided in UCAS-AOD-benchmark (The Reason might be training params for I only have RTX3060 12G)

files to be added :

• DOTA_devkit/ucas_aod_evaluation.py
• mmdet/datasets/UCAS_AOD.py
• tools/test.py
• configs/ucasaod/*

The first one is used when evaluating.
The second one is for loading custom dataset(like this directory in UCAS_AOD_Benchmark).
The third is adding params for evaluating.
The fourth is config file for training.

• 1.the processed dataset anno filed(.txt) have 14 cols, and they are $class,x_1,y_1,x_2,y_2,x_3,y_3,x_4,y_4,theta,x,y, width,height$. And theta is angle not arc(see here).

@article{han2021align,  
  author={J. {Han} and J. {Ding} and J. {Li} and G. -S. {Xia}},  
  journal={IEEE Transactions on Geoscience and Remote Sensing},   
  title={Align Deep Features for Oriented Object Detection},   
  year={2021}, 
  pages={1-11},  
  doi={10.1109/TGRS.2021.3062048}}

@inproceedings{xia2018dota,
  title={DOTA: A large-scale dataset for object detection in aerial images},
  author={Xia, Gui-Song and Bai, Xiang and Ding, Jian and Zhu, Zhen and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={3974--3983},
  year={2018}
}

@InProceedings{Ding_2019_CVPR,
  author = {Ding, Jian and Xue, Nan and Long, Yang and Xia, Gui-Song and Lu, Qikai},
  title = {Learning RoI Transformer for Oriented Object Detection in Aerial Images},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2019}
}

@article{chen2019mmdetection,
  title={MMDetection: Open mmlab detection toolbox and benchmark},
  author={Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and Liu, Ziwei and Xu, Jiarui and others},
  journal={arXiv preprint arXiv:1906.07155},
  year={2019}
}

pytorch1.9