Artificial intelligence-enabled defect detection method and engineering application of ceramic mug

Abstract

In the manufacturing process of ceramic mugs, the detection of micro-surface defects faces technical challenges of high difficulty and low efficiency, and efficient and high-quality production lines are crucial to maintaining market competitiveness. The goal of this study is to improve the accuracy and efficiency of detection by developing a defect detection algorithm and equipment based on deep learning, thereby improving product quality and reducing production costs. The research uses the visual algorithm You Only Look Once version 8 (YOLOv8) in the field of artificial intelligence as the baseline model. Firstly, a slice pre-training layer is designed to reduce the memory loss of large images to the graphics card. Secondly, the model structure is reconstructed to adapt to small target detection. In addition, a mixed local channel cross-stage feature fusion module is proposed to enhance the recognition ability of small targets. Finally, a detection head with shared parameters is designed to further reduce the number of parameters. In terms of engineering application, a set of test equipment was developed and the corresponding software was written. Experiments show that the accuracy of the algorithm is 21.3 % higher than that of YOLOv8, and the parameter amount is reduced by 67 %. Compared with manual detection, the equipment efficiency is increased by 47.06 %, and the detection success rate is 99.6 %. Therefore, the research in this paper provides an efficient and reliable solution for industrial automation detection.