对比YOLOv9/v10与RT-DETR在田间杂草检测中的精度与速度表现。
Comparative Analysis of YOLOv9, YOLOv10 and RT-DETR for Real-Time Weed Detection
- 在多种尺寸模型和分辨率下测试三类目标检测模型性能。
- 在640px分辨率下，YOLOv10小模型达到65.2% mAP，推理速度最快。
- 适合需要快速响应的智能喷洒系统开发者参考。
本文对YOLOv9、YOLOv10和RT-DETR三种先进目标检测模型在智能喷洒应用中杂草检测任务的表现进行了全面评估，重点关注糖菜、单子叶和双子叶三类作物。基于不同GPU和CPU设备上的平均精度（mAP）与推理时间进行对比，考察了nano、small、medium、large等模型变体及320px、480px、640px、800px、960px等多种图像分辨率下的性能。结果揭示了推理速度与检测精度之间的权衡关系，为实时杂草检测系统中模型选型提供了重要依据，助力精准农业管理与生产效率提升。
原文摘要 · Abstract (English)
This paper presents a comprehensive evaluation of state-of-the-art object detection models, including YOLOv9, YOLOv10, and RT-DETR, for the task of weed detection in smart-spraying applications focusing on three classes: Sugarbeet, Monocot, and Dicot. The performance of these models is compared based on mean Average Precision (mAP) scores and inference times on different GPU and CPU devices. We consider various model variations, such as nano, small, medium, large alongside different image resolutions (320px, 480px, 640px, 800px, 960px). The results highlight the trade-offs between inference time and detection accuracy, providing valuable insights for selecting the most suitable model for real-time weed detection. This study aims to guide the development of efficient and effective smart spraying systems, enhancing agricultural productivity through precise weed management.
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