RotaTR: Detection Transformer for Dense and Rotated Object
Zhu Yuke Ruan Yumeng Yang Lei Guo Sheng
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.