Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

Code

• github.com/Zzh-tju/DIoU
  OfficialIn paperpytorch★ 321
• github.com/PaddlePaddle/PaddleDetection
  paddle★ 14,132
• github.com/maudzung/Complex-YOLOv4-Pytorch
  pytorch★ 1,320
• github.com/Zzh-tju/CIoU
  pytorch★ 336
• github.com/frgfm/Holocron
  pytorch★ 328
• github.com/Zzh-tju/DIoU-darknet
  none★ 311
• github.com/Zzh-tju/DIoU-SSD-pytorch
  pytorch★ 89
• github.com/Zzh-tju/DIoU-pytorch-detectron
  pytorch★ 88
• github.com/gau-nernst/CenterNet
  pytorch★ 71
• github.com/wangermeng2021/Scaled-YOLOv4-tensorflow2
  tf★ 47

Abstract

Bounding box regression is the crucial step in object detection. In existing methods, while _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, , 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.

Tasks

Reproductions