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

TL;DR

This paper introduces Distance-IoU (DIoU) and Complete IoU (CIoU) losses that improve bounding box regression speed and accuracy in object detection by incorporating geometric factors, outperforming existing IoU-based losses.

Contribution

The paper proposes novel DIoU and CIoU loss functions that accelerate convergence and enhance bounding box regression accuracy in object detection models.

Findings

DIoU loss converges faster than IoU and GIoU losses.

CIoU loss incorporates aspect ratio and central point distance for better performance.

Integrating DIoU and CIoU into existing detectors improves IoU and GIoU metrics.

Abstract

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.