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 weaknesses of $IoU$ by introducing a generalized version 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.

本研究主要关注物体检测领域中最受欢迎的衡量标准之一的交并比（IoU），提出了一种广义的IoU（GIoU）作为新的回归损失和度量方式，并将其应用于现有的物体检测框架中，在PASCAL VOC和MS COCO等常见的物体检测基准测试中取得了一致的性能提升。

广义交并比: 一个边框回归的度量和损失函数