  Bounding box regression is the crucial step in object detection. In existing
methods, while $\ell_n$-norm loss is widely adopted for bounding box
regression, it is not tailored to the evaluation metric, i.e., Intersection
over Union (IoU). Recently, IoU loss and generalized IoU (GIoU) loss have been
proposed to benefit the IoU metric, but still suffer from the problems of slow
convergence and inaccurate regression. In this paper, we propose a Distance-IoU
(DIoU) loss by incorporating the normalized distance between the predicted box
and the target box, which converges much faster in training than IoU and GIoU
losses. Furthermore, this paper summarizes three geometric factors in bounding
box regression, \ie, overlap area, central point distance and aspect ratio,
based on which a Complete IoU (CIoU) loss is proposed, thereby leading to
faster convergence and better performance. By incorporating DIoU and CIoU
losses into state-of-the-art object detection algorithms, e.g., YOLO v3, SSD
and Faster RCNN, we achieve notable performance gains in terms of not only IoU
metric but also GIoU metric. Moreover, DIoU can be easily adopted into
non-maximum suppression (NMS) to act as the criterion, further boosting
performance improvement. The source code and trained models are available at
https://github.com/Zzh-tju/DIoU.
