Abstract
We present RiO-DETR: DETR for Real-time Oriented Object Detection, the first real-time oriented detection transformer to the best of our knowledge. Adapting DETR to oriented bounding boxes (OBBs) poses three challenges: semantics-dependent orientation, angle periodicity that breaks standard Euclidean refinement, and an enlarged search space that slows convergence. RiO-DETR resolves these issues with task-native designs while preserving real-time efficiency. First, we propose Content-Driven Angle Estimation by decoupling angle from positional queries, together with Rotation-Rectified Orthogonal Attention to capture complementary cues for reliable orientation. Second, Decoupled Periodic Refinement combines bounded coarse-to-fine updates with a Shortest-Path Periodic Loss for stable learning across angular seams. Third, Oriented Dense O2O injects angular diversity into dense supervision to speed up angle convergence at no extra cost. Extensive experiments on DOTA-1.0, DIOR-R, and FAIR-1M-2.0 demonstrate RiO-DETR establishes a new speed–accuracy trade-off for real-time oriented detection. Code will be made publicly available.
本文提出RiO-DETR，一种面向实时旋转目标检测的DETR框架。针对方向语义依赖、角度周期性和搜索空间扩大等挑战，设计了内容驱动的角度估计、旋转校正正交注意力、解耦周期细化以及定向密集O2O机制，在保持实时效率的同时提升角度收敛速度与检测精度。在DOTA-1.0、DIOR-R和FAIR-1M-2.0上的实验表明，该方法实现了新的速度-精度平衡。