改进了你只看一次8n算法为基础的旋转器态度检测,用于球形电机
Sili Zhou1,2,3, Guoli Li2, Qunjing Wang2
1School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China.
The Review of scientific instruments
|March 12, 2025
概括
这项研究介绍了一种增强的YOLOv8n算法,用于在永磁球形电机中精确检测转子态度. 改进的方法可以实现更快的推断时间,同时保持高精度,优于现有技术.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 转子态度检测 (RAD) 对于控制永久磁体球形电机 (PMSpM) 至关重要.
- 现有的RAD方法可能缺乏动态控制应用所需的实时性能.
- 对象检测算法为非接触态度估计提供了一个潜在的途径.
研究的目的:
- 开发一种改进的基于You Only Look Once v8n (YOLOv8n) 的方法,用于在PMSpM中检测旋转器态度.
- 为了提高PMSpM视觉RAD的实时性能和准确性.
- 将拟议的方法与现有的算法和基于接触的基准进行比较.
主要方法:
- 收集和注释用于PMSpM的自定义视觉图像数据集.
- 通过集成FasterNet和c2fFaster模块来改进YOLOv8n架构的实时处理.
- 使用定制和公共数据集 (COCO 2017) 增强的YOLOv8n模型的培训和验证.
- 改进的YOLOv8n方法与标准的YOLOv8n和多对象卡尔曼内核相关过器 (MKKCF) 算法的比较.
主要成果:
- 改进的YOLOv8n算法在定制的PMSpM数据集上实现了低至4.31ms的推断时间,确保了高检测准确性.
- 与原来的YOLOv8n和MKKCF算法相比,增强的算法表现出明显优异的性能.
- 该方法允许使用单个单眼相机的三个视觉对象计算PMSpM旋转器态度.
结论:
- 建议改进的基于YOLOv8n的RAD方法为PMSpM控制提供了高度准确和实时的解决方案.
- 改进的算法超过了基于MKKCF的视觉RAD方法的精度,通过空间运动实验验证实了这一点.
- 这种非接触式视觉RAD方法为PMSpM中精确的转子态度估计提供了强大的替代方案.
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