Regression loss design is an essential topic for oriented object detection. Due to the periodicity of the angle and the ambiguity of width and height definition, traditional L1-distance loss and its variants have been suffered from the metric discontinuity and the square-like problem. As a solution, the distribution based methods show significant advantages by representing oriented boxes as distributions. Differing from exploited the Gaussian distribution to get analytical form of distance measure, we propose a novel oriented regression loss, Wasserstein Distance(EWD) loss, to alleviate the square-like problem. Specifically, for the oriented box(OBox) representation, we choose a specially-designed distribution whose probability density function is only nonzero over the edges. On this basis, we develop Wasserstein distance as the measure. Besides, based on the edge representation of OBox, the EWD loss can be generalized to quadrilateral and polynomial regression scenarios. Experiments on multiple popular datasets and different detectors show the effectiveness of the proposed method.

回归损失设计是面向对象检测的一个重要课题，由于角度的周期性和宽度和高度定义的不确定性，传统的L1距离损失及其变种一直以来都面临着度量不连续和类似于方形的问题，分布基方法则通过将有方向的框表示为分布而显示出显著优势，我们提出了一种新颖的有方向回归损失，Wasserstein距离（EWD）损失，以缓解类似于方形的问题。同时，基于有方向框的边缘表示，EWD损失可以推广到四边形和多项式回归场景，对多个常用数据集和不同的检测器的实验表明了该方法的有效性。

面向定向物体检测的边缘瓦瑟斯坦距离损失