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.

• About the detailed installation, please see this CSDN Blog. (Thanks for author@SSSlasH of this blog).

• The code for MMRotate is available now.

• RepPoints + our APAA can obtain +2.5AP (36.3 to 38.8) improvement with R-50 on COCO dataset for general object detection.

Please refer to for installation and dataset preparation.

This repo is based on . Please see for the basic usage.

The results on DOTA test set are shown in the table below. More detailed results please see the paper.

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.

Note：Orientation error evaluation (mAOE) is calculated on the val subset(only train subset for training).

The visualization code for oriented bounding boxes and learning points is .

• Oriented bounding box

@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"
}

Here are some great resources we benefit. We would espeicially thank the authors of:

MMdetection

RepPoints

AerialDetection

BeyondBoundingBox