ABFL: Angular Boundary Discontinuity Free Loss for Arbitrary Oriented
  Object Detection in Aerial Images
Zifei Zhao Shengyang Li
Arbitrary oriented object detection (AOOD) in aerial images is a widely
concerned and highly challenging task, and plays an important role in many
scenarios. The core of AOOD involves the representation, encoding, and feature
augmentation of oriented bounding-boxes (Bboxes). Existing methods lack
intuitive modeling of angle difference measurement in oriented Bbox
representations. Oriented Bboxes under different representations exhibit
rotational symmetry with varying periods due to angle periodicity. The angular
boundary discontinuity (ABD) problem at periodic boundary positions is caused
by rotational symmetry in measuring angular differences. In addition, existing
methods also use additional encoding-decoding structures for oriented Bboxes.
In this paper, we design an angular boundary free loss (ABFL) based on the von
Mises distribution. The ABFL aims to solve the ABD problem when detecting
oriented objects. Specifically, ABFL proposes to treat angles as circular data
rather than linear data when measuring angle differences, aiming to introduce
angle periodicity to alleviate the ABD problem and improve the accuracy of
angle difference measurement. In addition, ABFL provides a simple and effective
solution for various periodic boundary discontinuities caused by rotational
symmetry in AOOD tasks, as it does not require additional encoding-decoding
structures for oriented Bboxes. Extensive experiments on the DOTA and HRSC2016
datasets show that the proposed ABFL loss outperforms some state-of-the-art
methods focused on addressing the ABD problem.