RT-SDGOD: Real-Time Single-Domain Generalized Object Detection

In real-world deployment under strict real-time constraints,weather and imaging variations induce significant distribution shifts,severely degrading detectors. Single-Domain Generalized Object Detec-tion aims to mitigate this issue, yet existing methods rarely investi-gate—at the level of problem formulation—the generalization capabil-ity of real-time detectors under such constrained inference budgets. Tothis end, we introduce Real-Time Single-Domain Generalized ObjectDetection (RT-SDGOD), which focuses on how real-time detectors canachieve cross-domain generalization under zero extra inference overheadby relying solely on training-time representation learning. We observethat, under domain shift, DETR-based real-time detectors mainly de-grade through increased missed detections, rooted in limited and un-stable object-level discriminative evidence. Based on this, we proposeRT-SDGDet, a multi-evidence collaborative modeling framework for RT-SDGOD. The core idea is to enable multiple queries of the same ob-ject to collaboratively cover more sufficient discriminative evidence whilemaintaining the stability of such evidence modeling across views. Specifi-cally, we use one-to-many (O2M) supervision to construct stable object-specific query groups, and further design Discriminative Evidence Di-versity Learning (DEDL) and Dual-view Evidence Consistency Learning(DvECL) to expand object-level evidence coverage and improve evidencestability under appearance perturbations, respectively. Since all compo-nents are introduced only during training, our method incurs no ex-tra inference overhead. Extensive experiments show that the proposedmethod achieves better generalization performance than existing ap-proaches across multiple unseen target domains.