  Detecting oriented objects along with estimating their rotation information
is one crucial step for analyzing remote sensing images. Despite that many
methods proposed recently have achieved remarkable performance, most of them
directly learn to predict object directions under the supervision of only one
(e.g. the rotation angle) or a few (e.g. several coordinates) groundtruth
values individually. Oriented object detection would be more accurate and
robust if extra constraints, with respect to proposal and rotation information
regression, are adopted for joint supervision during training. To this end, we
innovatively propose a mechanism that simultaneously learns the regression of
horizontal proposals, oriented proposals, and rotation angles of objects in a
consistent manner, via naive geometric computing, as one additional steady
constraint (see Figure 1). An oriented center prior guided label assignment
strategy is proposed for further enhancing the quality of proposals, yielding
better performance. Extensive experiments demonstrate the model equipped with
our idea significantly outperforms the baseline by a large margin to achieve a
new state-of-the-art result without any extra computational burden during
inference. Our proposed idea is simple and intuitive that can be readily
implemented. Source codes and trained models are involved in supplementary
files.
