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.
翻译:在目标检测中,捆绑框回归(BBR)是一个关键步骤,决定目标定位性性能。然而,我们发现,BBR以往的大部分损失功能存在两个主要缺陷:(一) 美元-美元-诺尔姆和IOU基础上的亏损功能都无法有效地描述BBR的目标,从而导致趋同速度和不准确的回归结果。 (二) 大多数损失功能忽视了BBR的不平衡问题,即与目标框有小部分重叠的大量锚箱对优化BBBR作用作用。为了减轻由此造成的不利效应,我们进行了彻底研究,以发掘BBBR损失的可能性。首先,提出了对UU(EOU)损失的有效交叉功能,以明确衡量BBBR(即重叠区域、中心点和侧面长度)的三个几何因素的差异。之后,我们陈述了有效示例采矿(EEM)问题,并提出一个焦点损失的回归版本,以高品质锁定锁定锁定的锚箱为焦点。最后,上述两个部分合起来,以获得BBBBB公司损失的潜在精确性交叉率。