Bounding Box Projection for Regression Uncertainty in Oriented Object Detection

Oriented object detection has recently attracted increasing attention for its importance in aerial image processing. Popular detection methods for oriented and densely packed objects usually utilize the rotation angle to reduce the overlap of bounding boxes over the horizontal line. However, those angle-based methods remain challenging due to the angular periodicity and regression inconsistent, which are uniformly summarized as regression uncertainty. Previous methods usually heal the regression uncertainty problem by adding complex limits on the loss function. In this paper, instead of expressing accurate oriented information by the rotation angle, we propose a novel projection-based method that employs six-parameter to describe rotated bounding box: two points position on the projected line(one center point and one chosen vertex), a projection ratio and a quadrant label, named as
ProjBB
. ProjBB is angle-free and has three main advantages: i) More accurate angle distance without periodicity; ii) Projection ratio which is aspect ratio sensitive; iii) Theoretical uniqueness guarantee. Extensive experiments on two large-scale public aerial image datasets(i.e. DOTA and HRSC2016) and the scene text dataset ICDAR2015 show the competitive results of our proposed approach.