VM-YOLO:YOLO与VMamba一起用于草花检测
Yujin Wang1, Xueying Lin1, Zhaowei Xiang1
1School of Mechanical Engineering, Chongqing University of Technology, Banan, Chongqing 400054, China.
Plants (Basel, Switzerland)
|February 13, 2025
概括
本研究介绍了VM-YOLO,这是一款用于智能农业中检测草花的轻量级计算机视觉模型. 对于资源有限的农业机械,VM-YOLO平衡了精度和计算效率.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉 计算机视觉
- 机器学习是机器学习.
背景情况:
- 计算机视觉对于非侵入性智能农业至关重要,但在机械上有限的计算资源方面面临着挑战.
- 为农业部署先进的视觉系统,优化准确度和计算能力的算法至关重要.
研究的目的:
- 开发一个轻量级的混合网络,VM-YOLO,用于高效地检测草花.
- 提高对具有有限计算能力的农业机械物体检测模型的性能.
主要方法:
- 提出了一个新的轻量化混合网络,VM-YOLO,集成一个Light C2f模块和一个VMambaNeck.
- 修改了YOLOv8架构,将C2f模块替换为Light C2f,用于多尺度特征感知,并将子替换为VMambaNeck,用于全局灵敏度.
- 在一个定制的草花数据集上训练和评估VM-YOLO,执行切除,多数据集和比较实验.
主要成果:
- 与基线YOLOv8.8相比,VM-YOLO在各种数据集中在对象检测方面表现出卓越的性能.
- 拟议的VM-YOLO实现了比YOLOv6,更快的R-CNN,FCOS和RetinaNet更好的平均精度 (mAP),更快的推断速度和更少的参数.
- 废弃性研究证实了Light C2f模块和VMambaNeck组件的有效性.
结论:
- 在智能农业中,VM-YOLO为实时草花检测提供了有效的解决方案,解决了计算限制.
- 混合网络设计在检测精度和计算效率之间提供了有利的权衡,适合农业应用.
- 这项研究通过优化深度学习模型以实现实际部署,有助于推进精密农业中的计算机视觉应用.
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