Object instances in remote sensing images often distribute with
multi-orientations, varying scales, and dense distribution. These issues bring
challenges to end-to-end oriented object detectors including multi-scale
features alignment and a large number of queries. To address these limitations,
we propose an end-to-end oriented detector equipped with an efficient decoder,
which incorporates two technologies, Rotated RoI attention (RRoI attention) and
Selective Distinct Queries (SDQ). Specifically, RRoI attention effectively
focuses on oriented regions of interest through a cross-attention mechanism and
aligns multi-scale features. SDQ collects queries from intermediate decoder
layers and then filters similar queries to obtain distinct queries. The
proposed SDQ can facilitate the optimization of one-to-one label assignment,
without introducing redundant initial queries or extra auxiliary branches.
Extensive experiments on five datasets demonstrate the effectiveness of our
method. Notably, our method achieves state-of-the-art performance on DIOR-R
(67.31% mAP), DOTA-v1.5 (67.43% mAP), and DOTA-v2.0 (53.28% mAP) with the
ResNet50 backbone.

我们提出了一种具备有效的解码器的端到端定向目标检测器，该解码器结合了旋转 RoI 注意力（RRoI 注意力）和选择性不同查询（SDQ）两项技术，能够有效地解决遥感图像中多方向、不同尺度、密集分布的目标实例的检测问题。通过交叉注意机制，RRoI 注意力能够有效地聚焦定向感兴趣区域并对多尺度特征进行对齐。通过从中间解码器层收集查询并过滤相似查询以获得不同查询，提出的 SDQ 能够促进一对一标签分配的优化，而不会引入多余的初始查询或额外的辅助分支。在五个数据集上进行的大量实验证明了我们方法的有效性，尤其是我们的方法在 DIOR-R（67.31% mAP）、DOTA-v1.5（67.43% mAP）和 DOTA-v2.0（53.28% mAP）上取得了最先进的性能，使用了 ResNet50 作为骨干网络。