Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition

The intelligent inspection of ceramic decorative defects is one of the hot research at present. This work aims to improve the defect inspection automation of finished decorative ceramic workpieces. First, it introduces the multi-target detection algorithm and compares the performance of different network models on the public data set. Second, the initial images are collected on the spot. The initial pictures are easy to produce noise in actual deployment, affecting the image quality. Therefore, image preprocessing is performed for the initial images, and a median filtering method is used to calculate the denoising. Finally, the original You Only Look Once version 3 network model is realized. Based on this, the decorative ceramic-oriented Automated Surface Defect Inspection model is proposed. Then, decorative ceramic defect images are inputted for model training. The experimental conclusions are deeply studied and analyzed. The results show that the proposed decorative ceramic-oriented Automated Surface Defect Inspection model based on Deep Learning technology has good feature extraction and inspection ability. The detection accuracy is 94.90% on the test set, and the detection speed reaches 25 frames per second. Compared with the traditional manual inspection method, the proposed model greatly improves the inspection effect and can meet the on-site inspection requirements of surface defects of decorative ceramics under complex backgrounds. It is of great significance to improve the quality inspection efficiency and economic benefits of China's decorative ceramics industry.