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基于自动编码的无监督表面缺陷检测,使用双阶段训练

Tesfaye Getachew Shiferaw1, Li Yao1,2

  • 1School of Computer Science and Engineering, Southeast University, Nanjing 211189, China.

Journal of imaging
|May 24, 2024
PubMed
概括

本研究介绍了一种无监督的表面缺陷检测方法,可以准确识别缺陷并重建干净的背景. 这种新的方法使用自适应加权结构相似性损失来改善特征学习,并取得了最先进的结果.

关键词:
人工缺陷的产生 人工缺陷的产生自动编码器自动编码器感知相似性 感知相似性结构上的相似性.表面缺陷检测检测表面缺陷检测

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相关概念视频

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...