In object detection, bounding box regression (BBR) is a crucial step that determines the object localization performance. However, we find that most previous loss functions for BBR have two main drawbacks: (i) Both $\ell_n$-norm and IOU-based loss functions are inefficient to depict the objective of BBR, which leads to slow convergence and inaccurate regression results. (ii) Most of the loss functions ignore the imbalance problem in BBR that the large number of anchor boxes which have small overlaps with the target boxes contribute most to the optimization of BBR. To mitigate the adverse effects caused thereby, we perform thorough studies to exploit the potential of BBR losses in this paper. Firstly, an Efficient Intersection over Union (EIOU) loss is proposed, which explicitly measures the discrepancies of three geometric factors in BBR, i.e., the overlap area, the central point and the side length. After that, we state the Effective Example Mining (EEM) problem and propose a regression version of focal loss to make the regression process focus on high-quality anchor boxes. Finally, the above two parts are combined to obtain a new loss function, namely Focal-EIOU loss. Extensive experiments on both synthetic and real datasets are performed. Notable superiorities on both the convergence speed and the localization accuracy can be achieved over other BBR losses.

中文翻译：

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准确有效的边界框回归的有效有效的IOU损失

在对象检测中，边界框回归（BBR）是确定对象定位性能的关键步骤。但是，我们发现，大多数先前的BBR损失函数都有两个主要缺点：（i）$ \ ell_n $ -norm和基于IOU的损失函数均无法有效描述BBR的目标，从而导致收敛缓慢且回归结果不准确。（ii）大多数损失函数都忽略了BBR中的不平衡问题，即与目标箱重叠较少的大量锚框对BBR的优化起了最大作用。为了减轻由此造成的不利影响，我们进行了透彻的研究，以探索本文中BBR损失的可能性。首先，提出了一种有效的联盟交集（EIOU）损失，它明确地衡量了BBR中三个几何因素的差异，即 重叠区域，中心点和边长。之后，我们陈述有效示例挖掘（EEM）问题，并提出焦点损失的回归版本，以使回归过程专注于高质量锚框。最后，将以上两个部分结合起来得到一个新的损失函数，即Focal-EIOU损失。在合成和真实数据集上都进行了广泛的实验。与其他BBR损耗相比，可以在收敛速度和定位精度上均具有明显的优势。在合成和真实数据集上都进行了广泛的实验。与其他BBR损耗相比，可以在收敛速度和定位精度上均具有明显的优势。在合成和真实数据集上都进行了广泛的实验。与其他BBR损耗相比，可以在收敛速度和定位精度上均具有明显的优势。