The Detection Transformer (DETR) has emerged as a pivotal role in object detection tasks, setting new performance benchmarks due to its end-to-end design and scalability. Despite its advancements, the application of DETR in detecting rotated objects has demonstrated suboptimal performance relative to established oriented object detectors. Our analysis identifies a key limitation: the L1 cost used in Hungarian Matching leads to duplicate predictions due to the square-like problem in oriented object detection, thereby obstructing the training process of the detector. We introduce a Hausdorff distance-based cost for Hungarian matching, which more accurately quantifies the discrepancy between predictions and ground truths. Moreover, we note that a static denoising approach hampers the training of rotated DETR, particularly when the detector's predictions surpass the quality of noised ground truths. We propose an adaptive query denoising technique, employing Hungarian matching to selectively filter out superfluous noised queries that no longer contribute to model improvement. Our proposed modifications to DETR have resulted in superior performance, surpassing previous rotated DETR models and other alternatives. This is evidenced by our model's state-of-the-art achievements in benchmarks such as DOTA-v1.0/v1.5/v2.0, and DIOR-R.

翻译：检测Transformer（DETR）在目标检测任务中发挥着关键作用，凭借其端到端设计和可扩展性树立了新的性能基准。尽管取得了进展，但DETR在旋转目标检测中的应用表现仍不及成熟的旋转目标检测器。我们的分析发现了一个关键局限：匈牙利匹配中使用的L1代价因旋转目标检测中的类方形问题导致重复预测，从而阻碍了检测器的训练过程。我们引入了一种基于豪斯多夫距离的代价用于匈牙利匹配，该方法能更精确地量化预测与真值之间的差异。此外，我们注意到静态去噪方法会阻碍旋转DETR的训练，尤其是当检测器的预测质量超过加噪真值后。我们提出了一种自适应查询去噪技术，通过采用匈牙利匹配选择性过滤不再有助于模型改进的多余加噪查询。我们对DETR提出的改进取得了卓越性能，超越了之前的旋转DETR模型及其他替代方案。这一点通过我们的模型在DOTA-v1.0/v1.5/v2.0及DIOR-R等基准测试中实现的最优结果得到了充分验证。