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Generalized Intersection over Union: A Metric and a Loss for Bounding Box Regression

Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, Silvio Savarese
CVPR 2019
doi:10.1109/CVPR.2019.00075 /cvpr/2019/rezatofighi2019cvpr-generalized/

Abstract

Intersection over Union (IoU) is the most popular evaluation metric used in the object detection benchmarks. However, there is a gap between optimizing the commonly used distance losses for regressing the parameters of a bounding box and maximizing this metric value. The optimal objective for a metric is the metric itself. In the case of axis-aligned 2D bounding boxes, it can be shown that IoU can be directly used as a regression loss. However, IoU has a plateau making it infeasible to optimize in the case of non-overlapping bounding boxes. In this paper, we address the this weakness by introducing a generalized version of IoU as both a new loss and a new metric. By incorporating this generalized IoU ( GIoU) as a loss into the state-of-the art object detection frameworks, we show a consistent improvement on their performance using both the standard, IoU based, and new, GIoU based, performance measures on popular object detection benchmarks such as PASCAL VOC and MS COCO.

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Text

Rezatofighi et al. "Generalized Intersection over Union: A Metric and a Loss for Bounding Box Regression." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00075

Markdown

[Rezatofighi et al. "Generalized Intersection over Union: A Metric and a Loss for Bounding Box Regression." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/rezatofighi2019cvpr-generalized/) doi:10.1109/CVPR.2019.00075

BibTeX

@inproceedings{rezatofighi2019cvpr-generalized,
  title     = {{Generalized Intersection over Union: A Metric and a Loss for Bounding Box Regression}},
  author    = {Rezatofighi, Hamid and Tsoi, Nathan and Gwak, JunYoung and Sadeghian, Amir and Reid, Ian and Savarese, Silvio},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2019},
  doi       = {10.1109/CVPR.2019.00075},
  url       = {https://mlanthology.org/cvpr/2019/rezatofighi2019cvpr-generalized/}
}