在小麦田中检测杂草的YOLOv8模型基于视觉转换器和多尺度特征融合
Yinzeng Liu1, Fandi Zeng1, Hongwei Diao1
1Mechanical and Electronic Engineering College, Shandong Agriculture and Engineering University, Jinan 250100, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
在小麦田中精确检测杂草的新型YOLOv8-MBM模型得到了改进. 这种模型提高了精度和回忆力,以更好地识别和控制杂草.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 准确的杂草检测对于在小麦田中精确的杂草控制至关重要.
- 挑战包括外观相似,以及杂草和小麦之间没有明确的尺寸区别.
- 现有的模型在农业环境中难以应对杂草识别的复杂性.
研究的目的:
- 为小麦田开发一个先进的杂草检测模型.
- 为了提高杂草识别的准确性和效率.
- 为了解决当前杂草检测技术的局限性.
主要方法:
- 小麦杂草数据集的构建.
- 开发了YOLOv8-MBM模型,一个增强的YOLOv8s架构.
- 集成MobileViTv3用于功能融合和BiFPN用于多尺度功能增强.
- 实施MPDIOU损失函数,以提高收性和性能.
主要成果:
- 该YOLOv8-MBM模型在杂草检测中实现了92.7%的准确性.
- 与YOLOv3,YOLOv5s和YOLOv9.9等主流型号相比,表现出卓越的性能.
- 与原来的YOLOv8s模型相比,精度 (+10.6%),召回 (+8.9%),mAP1 (+9.7%) 和mAP2 (+9.3%) 显著改善.
结论:
- YOLOv8-MBM模型有效地满足了在小麦种植中准确检测杂草的要求.
- 拟议的模型提供了增强的检测能力,有助于精准农业.
- 进一步的研究可以探索这种模式在作物管理中的更广泛应用.
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