Edge Wasserstein Distance Loss for Oriented Object Detection
Yuke Zhu Yumeng Ruan Zihua Xiong Sheng Guo
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.