Awesome
Oriented RepPoints for Aerial Object Detection
<img src="https://github.com/LiWentomng/OrientedRepPoints/blob/main/docs/overallnetwork.png" width="800px">Wentong Li, Yijie Chen, Kaixuan Hu, Jianke Zhu* (Arxiv)
- Based on OrientedRepPoints detector, the 2nd and 3rd Places are achieved on the Task 2 and Task 1 respectively in the “2021 challenge of Learning to Understand Aerial Images(LUAI)”. The detailed codes and introductions about it, please refer to this repository and 知乎.
Update
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About the detailed installation, please see this CSDN Blog. (Thanks for author@SSSlasH of this blog).
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The code for MMRotate is available now.
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RepPoints + our APAA can obtain +2.5AP (36.3 to 38.8) improvement with R-50 on COCO dataset for general object detection.
Installation
Please refer to for installation and dataset preparation.
Getting Started
This repo is based on . Please see
for the basic usage.
Results and Models
The results on DOTA test set are shown in the table below. More detailed results please see the paper.
| Model | Backbone | data aug(HSV+Rotation) | mAP | model | log |
|---|---|---|---|---|---|
| OrientedReppoints | R-50 | 75.97 | model | log | |
| OrientedReppoints | R-101 | 76.52 | model | log | |
| OrientedReppoints | Swin-Tiny | √ | 78.11 | model | log |
Note:
- The pretrained model--swin_tiny_patch4_window7_224 of Swin-Tiny for pytorch1.4.0 is here.
- We recommend to use our demo configs with 4 GPUs.
- The results are performed on the original DOTA images with 1024x1024 patches.
- The scale jitter is employed during training. More details see the paper.
The mAOE results on DOTA val set are shown in the table below.
| Model | Backbone | mAOE | Download |
|---|---|---|---|
| OrientedReppoints | R-50 | 5.93° | model |
Note：Orientation error evaluation (mAOE) is calculated on the val subset(only train subset for training).
Visual results
The visualization code for oriented bounding boxes and learning points is .
- Oriented bounding box
Citation
@inproceeding{orientedreppoints,
title="Oriented RepPoints for Aerial Object Detection.",
author="Wentong {Li}, Yijie {Chen}, Kaixuan {Hu}, Jianke {Zhu}.",
journal="The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
year="2022"
}
Acknowledgements
Here are some great resources we benefit. We would espeicially thank the authors of: