LCornerIoU: An Improved IoU-based Loss Function for Accurate Bounding Box Regression

Abstract

Bounding box regression is an important step in the process of object detection, which has a direct impact on the accuracy of model location. The $\ell_{n}$ loss is widely used in bounding box regression, but the criterion of locational accuracy is Intersection over Union (IOU), and there is no strong correlation between them. Although IoU-based loss functions have been proposed successively, such as IoU, GIoU, DIoU and CIoU, which make location loss directly related to evaluation criteria, there are still problems such as slow training convergence speed and inaccurate model location. Based on the IoU loss function, we add normalized corner distance and parameterized width height difference to form a new loss function: LCornerIoU loss. The results of bounding box regression simulation show that LCornerIoU loss is superior to other IoU-based loss functions in terms of convergence speed and positioning accuracy. Experiments on COCO benchmark show that the performance of the Faster R-CNN, RetinaNet, and YOLO-v5s models combined with LCornerIoU loss have consistent performance improvement compared with IoU loss.