Authors: Agastya Kalra, Guy Stoppi, Bradley Brown, Rishav Agarwal, Achuta Kadambi
Links: paper / supplement / video
We propose a mathematically sound rotation augmentation scheme in object detection that leads to better rotation invariance/equivariance that can be implemented in a few lines of code.
Here we provide the optimization code for maximizing expected IoU and deriving the ellipse using tensorflow and gradient descent in the optimization.ipynb file. For the RU Loss and Ellipse method we provide pseudo-code here and will be making pull requests soon.
Existing repos all use the largest box approach to rotating a bounding box, but this causes oversized boxes. We optimize for expected IoU and find that an ellipse shape
We then add a Rotation Uncertainty loss that allows the model to adapt the uncertain rotated label.
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If you find this work useful, please cite the paper as follows:
@inproceedings{kalra2021rotation,
title={Towards Rotation Invariance in Object Detection},
author={Agastya Kalra and Guy Stoppi and Bradley Brown and Rishav Agarwal and Achuta Kadambi},
journal={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
year={2021}
}