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相关概念视频

Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Quality Control01:05

Quality Control

Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
Quality Assurance01:19

Quality Assurance

Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
Control Systems01:10

Control Systems

Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...

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相关实验视频

Updated: Jun 26, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
11:34

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提高工业质量控制:一种转移学习方法,用于检测表面缺陷.

Ângela Semitela1,2, Miguel Pereira1, António Completo1,2

  • 1Centre of Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
概括

一个用于油漆加热装置表面的自动化系统结合了偏光度和明亮光成像来检测缺陷. 深度学习模型,特别是ResNet-50,准确地对表面进行分类,改善了工业质量控制.

关键词:
这就是ResNet-50的特点.自动化质量控制自动化质量控制检测和分类缺陷的发现和分类.照明方式 照明方式转移学习转移学习

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科学领域:

  • 工业自动化 工业自动化
  • 计算机视觉 计算机视觉
  • 材料科学 材料科学 材料科学

背景情况:

  • 自动化质量控制对于制造效率至关重要.
  • 由于缺陷类型和照明条件的不同,涂装表面的缺陷检测存在挑战.
  • 目前的方法可能缺乏稳定性或需要高计算资源.

研究的目的:

  • 开发和评估用于检测和分类加热器件涂漆表面缺陷的自动化系统.
  • 整合偏光度和明亮的光照明,以提高缺陷的识别.
  • 评估深度学习模型对缺陷分类的性能,并比较不同的算法方法.

主要方法:

  • 图像采集使用联合偏光度和明亮光照明.
  • 开发和培训深度学习模型 (定制,ResNet-50,Inception V3) 用于表面缺陷分类 (OK/NOK).
  • 在决策层面融合双模信息,并实施数据调度和可视化在线网络.

主要成果:

  • 双照明方法扩大了可检测缺陷的范围,同时通过决策级融合保持了低计算复杂性.
  • 预先训练的深度学习网络 (ResNet-50,Inception V3) 在准确性方面超过了定制构建的网络.
  • 在缺陷分类方面,ResNet-50实现了最高的准确性.
  • 该系统表现出一致的速度和准确性,特别是当模型与两种照明模式的数据进行训练时.
  • 表面信息已成功传输到服务器,用于图形用户界面可视化.

结论:

  • 开发的自动化系统有效地检测和分类涂料加热器件的表面缺陷.
  • 将偏光度和明亮光照明与深度学习,特别是ResNet-50相结合,为工业质量控制提供了强大而高效的解决方案.
  • 多模式信息的决策层面融合是保持准确性和计算效率的关键.