OrientedRepPoints

Oriented RepPoints employs a set of adaptive points to capture the geometric and spatial information of the arbitrary-oriented objects, which is able to automatically arrange themselves over the object in a spatial and semantic scenario. To facilitate the supervised learning, the oriented conversion function is proposed to explicitly map the adaptive point set into an oriented bounding box. Moreover, we introduce an effective quality assessment measure to select the point set samples for training, which can choose the representative items with respect to their potentials on orientated object detection. Furthermore, we suggest a spatial constraint to penalize the outlier points outside the groundtruth bounding box. In addition to the traditional evaluation metric mAP focusing on overlap ratio, we propose a new metric mAOE to measure the orientation accuracy that is usually neglected in the previous studies on oriented object detection. Experiments on three widely used datasets including DOTA, HRSC2016 and UCAS-AOD demonstrate that our proposed approach is effective.

Results and Models

The results on DOTA test set are shown in the table below(password:aabb). More detailed results please see the paper.

The mAOE results on DOTA val set are shown in the table below(password:aabb).

Note：

• Wtihout the ground-truth of test subset, the mAOE of orientation evaluation is calculated on the val subset(original train subset for training).
• The orientation (angle) of an aerial object is define as below, the detail of mAOE, please see the paper. The code of mAOE is mAOE_evaluation.py.

Visual results

The visual results of learning points and the oriented bounding boxes. The visualization code is .

• Learning points

• Oriented bounding box

Citation

@article{Li2021oriented,
  title={Oriented RepPoints for Aerial Object Detection},
  author={Wentong Li and Jianke Zhu},
  journal={arXiv preprint arXiv:2105.11111},
  year={2021}
}

GitHub

https://github.com/LiWentomng/OrientedRepPoints