R³Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object

Abstract

• R³Det is based on Focal Loss for Dense Object Detection, and it is completed by YangXue and ZhangGeFan.
• MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project developed by Multimedia Laboratory, CUHK.

Techniques:

• ResNet
• CUDA version of Feature Refinement Module (FRM) as PyTorch extension

Pipeline

Performance

DOTA1.0

Model	Backbone	Training data	Val data	mAP	Model Link	GPU	Image/GPU	Anchor	Reg. Loss	lr schd	Data Augmentation	Configs
R3Det*	ResNet50 600->800	DOTA1.0 trainval	DOTA1.0 test	71.90	Google Drive -- Baidu Drive (u8bj)	1X GeForce RTX 2080 Ti	6	H + R	smooth L1	2x	No	r3det_r50_fpn_2x_CustomizeImageSplit.py

R³Det*: R³Det with two refinement stages

Compile

python setup.py install

Train

sh rtools/train.sh

Or equivalent command:

python tools/train.py {configuration-file-path}

Before training, please:

1. Change the paths in lines 97-98 & 102-103 of dota_image_split.py according to your local DOTA dataset directory.
2. Run python dota_image_split.py to crop train & val set images into smaller tiles, and generate per-tile label files into the directories you specified in step [1].
3. Change the lines 4-10 of dotav1_rotational_detection.py. Paths in lines 5-8 shall direct to the folders containing the cropped image tiles and label files generated in step [2].
4. Have fun with sh rtools/train.sh and watch the model train!

Test

sh rtools/test.sh

Or equivalent command:

python tools/test.py {configuration-file-path} {checkpoint-file-path} --format-only --options submission_dir={path-to-save-submission-files}

Before test, please make sure the checkpoint file path (in rtools/test.sh) is correct.

Citation

If this is useful for your research, please consider cite.

@article{yang2019r3det,
    title={R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object},
    author={Yang, Xue et al},
    journal={arXiv preprint arXiv:1908.05612},
    year={2019}
}

@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 (CVPR)},
    pages={3974--3983},
    year={2018}
}

@article{mmdetection,
  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 Zhang, Zheng and Cheng, Dazhi and
             Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and
             Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong
             and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},
  journal= {arXiv preprint arXiv:1906.07155},
  year={2019}
}

Reference

• https://github.com/Thinklab-SJTU/R3Det_Tensorflow
• https://github.com/open-mmlab/mmdetection
• https://github.com/endernewton/tf-faster-rcnn
• https://github.com/zengarden/light_head_rcnn
• https://github.com/tensorflow/models/tree/master/research/object_detection
• https://github.com/fizyr/keras-retinanet