CA-IoU: Central-Gaussian Angle-IoU for Robust Bounding Box Regression

Abstract

Accurate object detection depends on the precise refinement of bounding box regression. Recent advancements in bounding box regression have introduced a variety of methodologies aimed at reducing the disparity between predicted and ground truth bounding boxes. The prevailing objective functions for bounding box regression typically encompass three key perspectives: i) Intersection over Union (IoU), ii) distance between central points, and iii) aspect ratio alignment. Nonetheless, these existing loss functions encounter two primary challenges including slow convergence of the distance term and aspect ratio variation irrelevant to bounding box localization. This paper presents two novel loss terms to address these challenges. Firstly, we introduce the concept of the Integral of Central-Gaussian, a novel approach that leverages the cumulative distribution function (CDF) derived from a closed-form Gaussian distribution based on the central points of bounding boxes. Secondly, we introduce an alternative aspect ratio representation by minimizing the angle between two bounding boxes in direct proportion to their IoU. We term this comprehensive loss function “Central-Gaussian Angle-IoU” (CA-IoU), seamlessly incorporating the Integral of Central-Gaussian with angle-based IoU. Extensive experiments on various models and benchmarks for object detection highlight the superior performance of CA-IoU loss compared to existing bounding box regression methods. The source code and the corresponding trained models will be made available.