Awesome
Learning Calibrated-Guidance for Object Detection in Aerial Images
Paper can be seen here arxiv
Introduction
This codebase is created to build benchmarks for object detection in aerial images. It is modified from mmdetection. The master branch works with PyTorch 1.1 or higher. If you would like to use PyTorch 0.4.1, please checkout to the pytorch-0.4.1 branch.
License
This project is released under the Apache 2.0 license.
- You can find the detailed configs in configs/DOTA.
- The trained models are available at Google Drive.
Installation
Please refer to INSTALL.md for installation.
Get Started
Please see GETTING_STARTED.md for the basic usage of mmdetection.
Contributing
We appreciate all contributions to improve benchmarks for object detection in aerial images.
Citing
If you use DOTA dataset, codebase or models in your research, please consider cite .
@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}
}
@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}
}
@misc{liang2021cgnet,
title= {Learning Calibrated-Guidance for Object Detection in Aerial Images},
author = {Dong Liang and Zongqi Wei and Dong Zhang and Qixiang Geng and Liyan Zhang and Han Sun and Huiyu Zhou and Mingqiang Wei and Pan Gao},
booktitle= {arXiv:2103.11399},
year = {2021}
}