With the publication of DINO, a variant of the Detection Transformer (DETR), Detection Transformers are breaking the record in the object detection benchmark with the merits of their end-to-end design and scalability. However, the extension of DETR to oriented object detection has not been thoroughly studied although more benefits from its end-to-end architecture are expected such as removing NMS and anchor-related costs. In this paper, we propose a first strong DINO-based baseline for oriented object detection. We found that straightforward employment of DETRs for oriented object detection does not guarantee non-duplicate prediction, and propose a simple cost to mitigate this. Furthermore, we introduce a novel denoising strategy that uses Hungarian matching to filter redundant noised queries and query alignment to preserve matching consistency between Transformer decoder layers. Our proposed model outperforms previous rotated DETRs and other counterparts, achieving state-of-the-art performance in DOTA-v1.0/v1.5/v2.0, and DIOR-R benchmarks.

本文针对DET向有向物体检测的扩展做出首例有力的DINO基线，并提出了一种用于缓解重复预测的简单成本和匈牙利匹配的新型去噪策略，该模型的性能在DOTA-v1.0 / v1.5 / v2.0和DIOR-R基准测试中均达到了最先进水平。

RHINO: 旋转DETR结合匈牙利匹配与动态降噪的目标检测方法