Review of Research on Ceramic Surface Defect Detection Based on Deep Learning

Ceramic surfaces are directly related to product quality and safety in industry, and any minor defects may affect performance. Therefore, surface defect detection has important practical significance. Traditional detection methods have limitations, while deep learning methods bring new opportunities. Although there have been many studies on ceramic surface detection, most of them focus on traditional image processing methods or single-angle deep learning applications. This article proposes a detection scheme that combines multi-perspective image acquisition and improved deep learning models for complex environments in industrial production lines, with a particular focus on small-sample, imbalance, and small-target defects. In ceramic defect detection, defects are often diverse, small in size, and difficult to collect, which can lead to insufficient model training and low recognition accuracy when using deep learning methods for defect detection. In addition, industrial production requires the high real-time performance of detection systems, which must respond quickly while ensuring accuracy to meet efficient and stable quality control requirements. Therefore, data imbalance, small samples, small targets, and real-time issues are particularly critical in ceramic defect detection. This article first introduces the basic steps and current situation of data preparation. It then explores solutions to the imbalanced-sample problem in ceramic surface defect detection using methods such as data augmentation, sample distribution optimization, network structure improvement, and loss function design. Additionally, it reviews the small-sample problem in ceramic surface defect detection through approaches like data augmentation, transfer learning, unsupervised learning, and network structure optimization. This article also elaborates on methods to enhance the detection accuracy of small-target defects on ceramic surfaces, including adding attention mechanisms, improving features, and optimizing network structures. Finally, it discusses improvements in the real-time performance of model defect detection from two perspectives: enhancing lightweight models and integrating and optimizing network modules. This article summarizes solutions for implementing ceramic surface defect-detection technology and explores future research directions in this field.