基于YOLOv5-MR模型的指针仪的自动识别读数方法
Le Zou1, Kai Wang1, Xiaofeng Wang1
1School of Artificial Intelligence and Big Data, Hefei University, Heifei 230601, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一种改进的YOLOv5-Meter Reading (YOLOv5-MR) 模型,用于准确的自动计数器读数. 增强型号在检测指针类型计时实现了更高的精度,减少了智能检查中的错误.
科学领域:
- 智能检查系统 智能检查系统
- 计算机视觉应用程序的应用程序
- 计量学和仪器仪表技术
背景情况:
- 目前使用目标检测的自动计数器读数方法的准确性低,错误很大.
- 指针类型的计数器需要精确的视觉解释以获得准确的读数.
研究的目的:
- 为了提高指针类型计表的自动计数读数的准确性.
- 开发一个改进的目标检测模型,用于仪表阅读应用程序.
主要方法:
- 提出了一种基于YOLOv5-Meter Reading (YOLOv5-MR) 模型的自动读取方法.
- 集成了一个多尺度目标检测层,并设计了定制,以改进小目标检测.
- 优化了损失函数和增量采样方法,以实现更快的模型融合.
- 开发了一种新的外部圆圈配套方法,并使用中心角算法来计算拨号读数.
主要成果:
- 在自建数据集上,YOLOv5-MR模型实现了79%的平均精度 (mAP).
- 与标准YOLOv5模型相比,显示了mAP的3%的改善.
- 在实验评估中表现优于现有的先进指针类型计数器读数模型.
结论:
- 拟议的YOLOv5-MR模型显著提高了指针类型计表的自动计数器读数的准确性.
- 在目标检测,模型训练和读数计算方面的改进有助于提高性能.
- 这种方法为涉及计数器读数的智能检查任务提供了更可靠的解决方案.
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