Progress in Deep Learning for Surface Defect Detection of Ceramics

Aiming at the problem of ceramic surface defect detection, deep learning algorithm is one of the hot spots in recent research. By establishing suitable data sets, selecting appropriate network models and algorithms, automatic detection and classification of ceramic surface defects can be realized. Commonly used deep learning surface defect detection algorithms include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Multilayer Perceptron (MLP), etc. Among them, the ceramic defect detection method based on YOLOv5 algorithm is a relatively advanced method in recent years, which has high detection accuracy and real-time performance, can accurately detect and identify various defects on the surface of ceramics and can further improve the performance of the algorithm by optimizing the network structure and loss function. The ceramic defect detection method based on CSS algorithm is to use the image segmentation method to segment ceramic defect samples and perform binary processing on the segmented sample set images to highlight the position and size of the defects. This paper was aimed to review the research progress in deep learning for surface defect detection of ceramics,introduce ceramic defect detection methods based on deep learning algorithms and summarize the process of ceramic surface defect detection algorithms based on YOLOv5 and CSS.