  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.
