Detecting the objects in dense and rotated scenes is a challenging task.
Recent works on this topic are mostly based on Faster RCNN or Retinanet. As
they are highly dependent on the pre-set dense anchors and the NMS operation,
the approach is indirect and suboptimal.The end-to-end DETR-based detectors
have achieved great success in horizontal object detection and many other areas
like segmentation, tracking, action recognition and etc.However, the DETR-based
detectors perform poorly on dense rotated target tasks and perform worse than
most modern CNN-based detectors. In this paper, we find the most significant
reason for the poor performance is that the original attention can not
accurately focus on the oriented targets. Accordingly, we propose Rotated
object detection TRansformer (RotaTR) as an extension of DETR to oriented
detection. Specifically, we design Rotation Sensitive deformable (RSDeform)
attention to enhance the DETR's ability to detect oriented targets. It is used
to build the feature alignment module and rotation-sensitive decoder for our
model. We test RotaTR on four challenging-oriented benchmarks. It shows a great
advantage in detecting dense and oriented objects compared to the original
DETR. It also achieves competitive results when compared to the
state-of-the-art.

利用旋转敏感形变注意力来增强 DETR 在面向检测任务中对旋转目标的检测能力，进而提出了旋转物体检测 Transformer（RotaTR）作为 DETR 的扩展模型，相比原始 DETR，在检测密集和旋转对象方面显示出巨大优势，并与现有最先进的模型相比取得了竞争性的结果。