YOLOv10-kiwi:一个基于YOLOv10的轻量级果检测模型,用于状果园中的果园
Jie Ren1, Wendong Wang1, Yuan Tian1
1College of Mathematics and Computer Science, Yan'an University, Yan'an, Shaanxi, China.
Frontiers in plant science
|September 10, 2025
概括
本研究介绍了YOLOv10-Kiwi,这是一款用于在农业机器人上实时检测果的轻量级模型. 它大大降低了模型尺寸和计算成本,同时保持了自动化果园任务的高精度.
科学领域:
- 计算机视觉 计算机视觉
- 农业技术 农业技术
- 机器学习 机器学习
背景情况:
- 实时检测基维果对于果园自动化至关重要.
- 现有的模型往往缺乏对资源有限的农业机器人的效率.
研究的目的:
- 开发一个轻量级和高效的果检测模型,用于杆式果园.
- 为了优化该模型在低功率农业机器人上部署.
主要方法:
- 一个紧的YOLOv10n架构被调整为减少缩放因子.
- 一个新的C2fDualHet模块用异质内核卷积 (HetConv) 层取代了传统结构.
- 整合了一个跨道融合模块 (CCFM),以加强功能集成.
- 使用MPDIoU损失函数来改善边界框回归和收.
主要成果:
- YOLOv10-Kiwi实现了2.02 MB的模型大小,0.51M的参数,以及2.1 GFLOPs.
- 该模型在一个定制的果数据集上显示了93.6%的mAP@50和74 FPS的推断速度.
- 与YOLOv10n基线相比,观察到模型大小 (80.34%),参数 (81.11%) 和GFLOPs (68.18%) 的显著减少.
结论:
- YOLOv10-Kiwi为自动化果检测提供了一个高效的解决方案.
- 该模型适合在铁杆式果园中的低功率农业机器人上部署.
- 建议的轻量化技术和模块有效平衡效率和准确性.
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