Method for detecting surface defects of ceramic tableware based on deep learning

Abstract

In view of the advantages of simple and high-precision detection methods for surface defects of ceramic tableware based on machine vision, the various links involved in such methods are reviewed. First, it summarizes the various imaging methods and common defect types on the surface of ceramic tableware; secondly, introduces and analyses the existing detection methods according to different mathematical modelling ideas; finally, summarizes the content the future research on the detection method of surface defects of ceramic tableware is prospected. It can be seen that the surface defect detection method of ceramic tableware based on machine vision has made great progress, but there is still room for improvement in the design of feature extraction algorithms, such as the feature extraction algorithm based on deep neural networks.