CEN‐RTDETR: A Co‐Enhancement‐Based Real‐Time Single‐Domain Generalized Object Detection for Road Scenes

ABSTRACT In urban road scenes, the cross‐domain data distribution differences caused by light and weather changes make the generalization performance of single‐domain trained object detectors in unknown weather scenarios significantly degraded (e.g., daytime‐sunny trained models in dusk‐rainy scenarios reduce the detection accuracy by more than 46% on average); moreover, faster R‐CNN suffers from insufficient generalization capability and inefficient real‐time inference due to architectural constraints. To address the above challenges, this paper proposes the CEN‐RTDETR method to improve the single‐domain generalization capability through a collaborative enhancement strategy: CP‐Mix color channel permutation dynamically simulates the RGB color channel permutation to simulate the multi‐sky color bias phenomenon, and enhances the color robustness of the input data; NP feature normalization perturbation applies a random feature perturbation to the channel statistics of the shallow feature map to optimize the extraction of texture, color and other basic style features; CORAL Loss minimizes the difference in feature distribution between the source domain and the virtual target domain through second‐order statistical matching. The experimental results show that CEN‐RTDETR achieves significant performance improvement on the cross‐weather scenario dataset DWD: The mean average precision (mmAP@0.5) across different weather scenarios increases from 39.66% to 42.72% (+3.06%), especially for dusk‐rainy and night‐rainy scenarios, where mAP@0.5 rises from 32.9% and 18.6% to 39.0% (+6.1%) and 24.1% (+5.5%) in extreme weather. The method in this paper effectively solves the cross‐domain generalization and efficiency problems in single‐domain generalized object detection, which provides new technical possibilities for real‐time detection in complex urban road scenes.