Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression
Open accessAbstract
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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GIoU improved the case when IoU loss could not optimize non-overlapping bounding boxes, i.e., IoU = 0.
“GIoU (Generalized IoU) [106] improved the case when IoU loss could not optimize the non-overlapping bounding boxes, i.e., IoU = 0 .”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [106]
The most intuitive improvement is to directly use IoU as the localization loss.
“The most intuitive improvement is to directly use IoU as the localization loss [105-107, 190].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [106]
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- Authors
- Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S.
- Affiliations
- The University of Adelaide (AU)
- Stanford University (US)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 658-666
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2950628590
- Cited by
- 6,612
- References
- 26
- Citations by year
- 2026: 765 · 2025: 1378 · 2024: 1400 · 2023: 1236 · 2022: 954 · 2021: 593 · 2020: 242 · 2019: 43 · 2018: 1
- FWCI
- 149.40
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- bounding box regression, object detection, regression loss, evaluation metrics, axis-aligned bounding boxes, metric optimization
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