  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.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning