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Updated: Jul 12, 2025

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对深度学习进行系统审查,使用CNN应用于表面缺陷检测.

Esteban Cumbajin1, Nuno Rodrigues1, Paulo Costa1

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

Journal of imaging
|October 27, 2023
PubMed
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此摘要是机器生成的。

本综述使用机器学习对表面缺陷检测进行了分类,重点关注各种工业表面的卷积神经网络 (CNN). 金属表面是最受研究的,分类是主要任务,转移学习被广泛使用.

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:

  • 表面缺陷检测在工业中至关重要,但信息碎片化阻碍了进步.
  • 机器学习,特别是卷积神经网络 (CNN),提供了先进的解决方案.
  • 需要一个结构化的概述来指导研究和应用.

研究的目的:

  • 系统地审查和分类基于机器学习的表面缺陷检测方法.
  • 专注于CNN,并根据表面类型 (金属,建筑,陶,木材,特殊) 将它们分类.
  • 根据审查结果提出一种新的机器学习分类学.

主要方法:

  • 按照PRISMA指南进行系统的文献审查.
  • 分析了59项主要研究,重点关注缺陷类型,表面类别和CNN架构.
  • 提取和总结关键特征:表面类型,问题,网络,技术,数据集和挑战.

主要成果:

  • 金属表面是最常见的 (62.71%),被归类为主要问题 (49.15%).
  • 转移学习 (83.05%) 和数据增强 (59.32%) 是被广泛采用的技术.
  • 提供了关于摄像机选择,照明策略和为现实世界应用程序创建数据集的见解.
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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结论:

  • 该审查提供了使用CNN进行表面缺陷检测的结构化分类.
  • 已识别的趋势和新的分类学为未来的研究提供了方向.
  • 为研究人员和缺陷检测专业人员提供有效的信息检索.