A novel approach of ceramic tile crack detection using morphological operations

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.

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“…In equation ( 8), n of s (n+1)/2 is an odd number. In equation (9), n of (s n/2 + s n/2+1 )/2 is an even number.…”

Section: Median Filteringmentioning

confidence: 99%

“…The rapid development of the foreign MV industry is due to the unremitting efforts of countless foreign researchers. Younas et al developed a unique architecture and algorithm set to realize the ASDI of continuous casting and hot rolling billets in steel plants [ 9 ]. Nogay et al constructed an MV-based ASDI system.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Jin

Wang

2022

Computational Intelligence and Neuroscience

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A comparison between the Bayesian network model and the logistic regression model in prevention of the defects on ceramic tiles

Sevinç

Kırca

2022

Journal of Experimental & Theoretical Artificial Intelligen

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One of the most important problems occurring in ceramic tile industry is defective product problem.Defective ceramics leads to loss of income and waste of resources in enterprises. However, it is generally unknown which factors and production stages cause what kinds of defects. On the other hand, in the literature, the existing modeling studies usually consider the defects seen on industrial ceramics. The defect types of industrial ceramics and those of ceramic tiles are different. Moreover, the classi cation types encountered in both kinds of ceramics also differ from each other. This article investigates the reasons behind the defect occurrences on ceramic tiles, along with a comparison between a logistic regression model and a Bayesian network model. In the study, the model validation results of the logistic regression model and the Bayesian network model show that the Bayesian network model is more successful in estimating the defect types. Thus, the constructed Bayesian network model indicates that in general, the high speeds of the production band signi cantly increase the probabilities of all kinds of defects except the deformation defect. Additionally, the high densities of the glaze also increase the occurrence levels of the defects, except the deformation defect. Similarly, the high levels of the engobe weight and the engobe density are also among the factors increasing the defect occurrences.

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A novel approach of ceramic tile crack detection using morphological operations

Cited by 2 publications

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

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

A comparison between the Bayesian network model and the logistic regression model in prevention of the defects on ceramic tiles

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