Research progress on unsupervised learning detection methods for visual surface defects
Visual surface defect detection is an important part of industrial production and quality control,in which the unsupervised learning paradigm of detection methods is an important development trend.This paper addresses the problem of unsupervised learning detection methods for visual surface defects in industrial production,quality control practical applications,and systematically introduces the current domestic and foreign major object surface defects dataset as well as defects in the visual detection method of the main evaluation index.Reviews the classification,fundamentals and framework,and application performance of the image reconstruction paradigm,generative model paradigm,and feature embedding paradigm for unsupervised learning detection of visual surface defects.This paper summarizes and compares the application characteristics of various methods and the development trends of technology,and points out that research on unsupervised visual surface defect detection such as normalized flow models and pre-trained large models deserves attention.
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