基于改进的YOLOv5的轻量计指针识别方法
Chi Zhang1, Kai Wang1, Jie Zhang2
1School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
这项研究介绍了YOLOv5-MRL,一种轻量级的物体检测模型,用于变电站的自动计量器读取. 它实现了高精度和速度,克服了实时监控传统模型的局限性.
科学领域:
- 计算机视觉 计算机视觉
- 电气工程 电气工程
- 人工智能的人工智能
背景情况:
- 经典的物体检测模型对于变电站监控硬件来说太大,速度太慢.
- 现有的轻量级模型难以平衡计量器读数的准确性和实时性能.
- 变电站自动计量器读数需要高效准确的物体检测.
研究的目的:
- 开发一个轻量级的物体检测算法 (YOLOv5-MRL) 用于变电站计数读数.
- 提高检测速度和准确性,以便部署在变电站监控设备上.
- 通过新的外部圆圈配件和圆角算法来促进表盘读取.
主要方法:
- 通过提高YOLOv5的速度和精度,构建了一个轻量级的YOLOv5-MRL算法.
- 应用卷积内核通道软修剪,以减少YOLOv5-MRL模型参数.
- 开发了一种表盘外圈装配方法,用于表盘读取的圆形角度算法.
主要成果:
- YOLOv5-MRL的平均精度达到了96.9%.
- 该模型显示了5ms/frame的快速检测速度.
- 模型的重量大小减少到5.5 MB,优于其他拨号读取模型.
结论:
- YOLOv5-MRL为变电站计数读数,平衡精度和实时需求提供了卓越的解决方案.
- 修剪和拨号读取算法可以在资源有限的硬件上高效地部署.
- 这种方法显著提高了电力变电站的自动计量器读数能力.
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