  Existing oriented object detection methods commonly use metric AP$_{50}$ to
measure the performance of the model. We argue that AP$_{50}$ is inherently
unsuitable for oriented object detection due to its large tolerance in angle
deviation. Therefore, we advocate using high-precision metric, e.g. AP$_{75}$,
to measure the performance of models. In this paper, we propose an Aspect Ratio
Sensitive Oriented Object Detector with Transformer, termed ARS-DETR, which
exhibits a competitive performance in high-precision oriented object detection.
Specifically, a new angle classification method, calling Aspect Ratio aware
Circle Smooth Label (AR-CSL), is proposed to smooth the angle label in a more
reasonable way and discard the hyperparameter that introduced by previous work
(e.g. CSL). Then, a rotated deformable attention module is designed to rotate
the sampling points with the corresponding angles and eliminate the
misalignment between region features and sampling points. Moreover, a dynamic
weight coefficient according to the aspect ratio is adopted to calculate the
angle loss. Comprehensive experiments on several challenging datasets show that
our method achieves competitive performance on the high-precision oriented
object detection task.
