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基于深度学习的指针计读数识别,以促进制造业数字化转型研究

Xiang Li1, Jun Zhao2, Changchang Zeng3

  • 1School of Mechanical Engineering, Sichuan University, Chengdu 610065, China.

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|January 11, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种先进的指针计读取识别 (PMRR) 系统,用于制造数据监控. 这种新的方法显著提高了数字化转型倡议的准确性和稳定性.

关键词:
这是PMRR.深度学习是一种深度学习.数字化转型数字化转型图像处理是图像处理的过程.模式识别 模式识别 模式识别

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 工业自动化 工业自动化

背景情况:

  • 制造业的数字化转型需要精确的数据监控.
  • 指针计读数识别 (PMRR) 对于工业数据收集至关重要.
  • 现有的PMRR方法与模糊,倾斜和复杂的背景等图像变化作斗争,限制了准确性和稳定性.

研究的目的:

  • 开发一个端到端的PMRR方法,克服当前方法的局限性.
  • 为了提高工业环境中指针计读数识别的准确性和稳定性.
  • 通过可靠的数据采集来支持制造系统的数字化转型.

主要方法:

  • 基于YOLOX探测器的脱圆头检测算法 (YOLOX-DC) 设计用于准确的指针拨号检测.
  • 采用类似Unet的纯变压器细分网络 (PM-SwinUnet) 与窗口多头注意力来增强特征提取.
  • 在PM-SwinUnet中集成了一个尺度和指针安装模块,以实现精确的定位,并使用了改进的角度方法来计算读数.

主要成果:

  • 与传统的矩形头相比,YOLOX-DC算法证明了指针计表盘的优异检测.
  • PM-SwinUnet有效地解决了错过的尺度检测和不完整的指针细分,改善了特征提取.
  • 拟议的端到端方法在三个不同的指针计数据集中实现了卓越的性能.

结论:

  • 开发的端到端PMRR方法为指针计读数的准确性和稳定性提供了显著的改进.
  • 这种方法为制造业数字化转型中的数据监控提供了快速可靠的解决方案.
  • 该研究强调了将先进的深度学习模型集成到工业自动化挑战中的潜力.