RiO-DETR: DETR for Real-time Oriented Object Detection

We present RiO-DETR: DETR for Real-time OrientedObject Detection, the first real-time oriented detection transformer tothe best of our knowledge. Adapting DETR to oriented bounding boxes(OBBs) poses three challenges: semantics-dependent orientation, angleperiodicity that breaks standard Euclidean refinement, and an enlargedsearch space that slows convergence. RiO-DETR resolves these issueswith task-native designs while preserving real-time efficiency. First, wepropose Content-Driven Angle Estimation by decoupling angle from po-sitional queries, together with Rotation-Rectified Orthogonal Attentionto capture complementary cues for reliable orientation. Second, Decou-pled Periodic Refinement combines bounded coarse-to-fine updates witha Shortest-Path Periodic Loss for stable learning across angular seams.Third, Oriented Dense O2O injects angular diversity into dense super-vision to speed up angle convergence at no extra cost. Extensive ex-periments on DOTA-1.0, DIOR-R, and FAIR-1M-2.0 demonstrate RiO-DETR establishes a new speed–accuracy trade-off for real-time orienteddetection. Code is available at https://github.com/RicePasteM/RiO-DETR.