Accelerating Convergence in Bounding Box Regression with a Refined IoU Loss Function

Abstract

Bounding box regression (BBR) is a critical component in object detection, significantly influencing the accuracy of object localization. However, existing Intersection over Union (IoU)-based loss functions encounter two primary challenges: (i) The penalty factor configuration often results in the expansion of anchor boxes during the regression, which in turn slows the convergence rate of the loss. (ii) There is a spatial imbalance caused by the disproportionate influence of anchor boxes with minimal overlap with the ground truth boxes. To resolve these two challenges, this paper proposes a novel loss function termed Fast-IoU, designed to swiftly and precisely measure the overlap area and aspect ratio in BBR. Building upon this, a dynamic non-monotonic focusing mechanism is integrated to evaluate the quality of anchor boxes in a non-linear manner. Fast-IoU can enhance the capability to focus on anchor boxes of medium quality. By incorporating Fast-IoU into popular object detectors such as YOLOv7, YOLOv8 and YOLOv10, we achieved an increase in average precision and improved performance compared to their original loss functions on the MS COCO datasets, thus validating the effectiveness of ourproposed improvement strategies.