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Oriented RepPoints for Aerial Object Detection

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

  • 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.

Installation

Please refer to install.md for installation and dataset preparation.

Getting Started

This repo is based on mmdetection. Please see getting_started.md 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 here.

  • 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:

MMdetection

RepPoints

AerialDetection

BeyondBoundingBox