YOLOv9-T专家混合模型动态分配任务，提升检测精度与召回率。

YOLO Meets Mixture-of-Experts: Adaptive Expert Routing for Robust Object Detection

• 多个YOLOv9-T专家按需分配，实现特征动态专精
• mAP和AR均优于单个YOLOv9-T模型
• 适合需要高精度、强鲁棒性的目标检测场景

本文提出一种新型的专家混合（Mixture-of-Experts）框架用于目标检测，通过在多个YOLOv9-T专家间实现自适应路由，使模型能够根据输入动态分配任务，从而实现特征的动态专精。该方法在保持高效性的同时，显著提升了平均精度（mAP）和平均召回率（AR），相较单一的YOLOv9-T模型表现出更优的检测性能。

原文摘要 · Abstract (English)

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.

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