Deep learning-based vehicle detection methods have achieved impressive performance in favorable conditions. However, their effectiveness declines significantly in adverse weather scenarios, such as fog, rain, and low-illumination environments, due to severe image degradation. Existing approaches often fail to achieve efficient integration between image enhancement and object detection, and typically lack adaptive strategies to cope with diverse degradation patterns. To address these challenges, this paper proposes a novel end-to-end detection framework, You Only Look Once-Dynamic Enhancement Routing (YOLO-DER), which introduces a lightweight Dynamic Enhancement Routing module. This module adaptively selects the optimal enhancement strategy—such as dehazing or brightness correction—based on the degradation characteristics of the input image. It is jointly optimized with the YOLOv12 detector to achieve tight integration of enhancement and detection. Extensive experiments on BDD100K, Foggy Cityscapes, and ExDark demonstrate the superior performance of YOLO-DER, yielding mAP50 scores of 80.8%, 57.9%, and 85.6%, which translate into absolute gains of +3.8%, +2.3%, and +2.9% over YOLOv12 on the respective datasets. The results confirm its robustness and generalization across foggy, rainy, and low-light conditions, providing an efficient and scalable solution for all-weather visual perception in autonomous driving.
Gao et al. (Wed,) studied this question.