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基于计算机视觉和深度学习的陶件制造过程的实时自动缺陷检测系统.

Esteban Cumbajin1, Nuno Rodrigues1, Paulo Costa1

  • 1Computer Science and Communications Research Centre, School of Technology and Management, Polytechnic of Leiria, 2411-901 Leiria, Portugal.

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概括
此摘要是机器生成的。

本研究介绍了使用计算机视觉和深度学习的陶制造业自动缺陷检测系统. 开发的系统在生产过程中能够高准确地识别陶件的缺陷.

关键词:
在美国,CNN是CNN.自动地表检查自动地表检查深度学习是一种深度学习.发现缺陷检测检测缺陷检测工业表面工业表面表面质量检查质量检查检查

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机科学 计算机科学
  • 工业工程 工业工程 工业工程

背景情况:

  • 自动缺陷检测对于工业质量控制至关重要.
  • 需要专门的技术来检查像陶这样的独特表面.
  • 现有的方法往往缺乏精度,用于陶异常检测.

研究的目的:

  • 为陶件提出和开发一个先进的缺陷检测解决方案.
  • 为实时工业应用实施具有深度学习的计算机视觉系统.
  • 通过自动化检查,加强陶制造业的质量控制.

主要方法:

  • 图像采集和一个专门的标签平台用于数据集创建.
  • 适用于陶表面分析的图像预处理技术.
  • 卷积神经网络 (CNN) 用于实时缺陷识别.

主要成果:

  • 该系统在检测缺陷方面实现了98.00%的准确性.
  • 97.29%的F1评分证明了该系统的有效性.
  • 在现实世界的工业环境中成功实施 (餐具制造).

结论:

  • 开发的自动化系统显著改善了陶件的缺陷检测.
  • 计算机视觉和深度学习方法为专门的表面提供了精确有效的解决方案.
  • 这项技术提高了陶制造业的质量控制和效率.