This paper presents a novel Mixture-of-Experts framework for object detection, incorporating adaptive routing among multiple YOLOv9-T experts to enable dynamic feature specialization and achieve higher mean Average Precision (mAP) and Average Recall (AR) compared to a single YOLOv9-T model.

翻译：本文提出了一种新颖的专家混合目标检测框架，该框架通过多个YOLOv9-T专家模型之间的自适应路由，实现了动态特征专业化。与单一YOLOv9-T模型相比，该框架获得了更高的平均精度均值（mAP）与平均召回率（AR）。