在边缘设备上使用YOLO-BRFB进行实时和资源高效的香束检测和定位
Shuo Wang1,2,3, Lijiao Wei1, Danran Zhang1,4
1Agricultural Machinery Research Institute, Chinese Academy of Tropical Agricultural Sciences, Guangdong, China.
Frontiers in plant science
|September 8, 2025
概括
这项研究介绍了YOLO-BRFB,这是一种轻量级的系统,用于检测和定位果园中的香束. 它在边缘设备上实现了高精度和实时性能,使自主收获成为可能.
科学领域:
- 农业机器人农业机器人
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 自主收获需要精确的香束检测和定位.
- 现有的方法是计算密集和复杂的.
- 挑战包括小目标和混乱的环境.
研究的目的:
- 开发一种轻量级,精确的香检测和3D定位系统.
- 优化系统,在现实世界农业环境中部署边缘设备.
- 加强自主收获技术.
主要方法:
- 将BasicRFB模块集成到YOLOv8框架中,以改进特征提取.
- 使用双眼视觉系统进行准确的3D空间坐标估计.
- 优化边缘设备部署,专注于实时处理和最小资源使用.
主要成果:
- YOLO-BRFB实现了高性能指标:精度 (0.957),回忆 (0.922),mAP (0.961) 和F1得分 (0.939).
- 与YOLOv8.8相比,证明了更好的回忆和mAP.
- 实现了较低的平均定位误差 (X: 12.33 mm,Y: 11.11 mm,Z: 16.33 mm) 和快速推断时间 (8.6 ms).
结论:
- YOLO-BRFB为精密收获提供了一种实用且具有成本效益的解决方案.
- 轻量级的设计适用于边缘计算设备,促进现实世界农业应用.
- 该系统推动了自主收获技术的发展.
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