Application of machine vision-based NDT technology in ceramic surface defect detection – a review

Dong, Guanping; Sun, Sheng; Wang, Zixi; Wu, Nanshou; Huang, Pingnan; Feng, Hao; Pan, Mei

doi:10.1515/mt-2021-2012

Cited by 9 publications

(6 citation statements)

References 92 publications

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“…Equation (7) indicates that the ReLU function has unilateral inhibition, generating sparsity among neurons. When the input value is positive, ReLU output equals the input, and the derivative is 1.…”

Section: Activation Functionmentioning

confidence: 99%

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

Jin

Wang

2022

Computational Intelligence and Neuroscience

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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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Section: Activation Functionmentioning

confidence: 99%

“…It needs to manually design feature extraction operators to extract target features in the image and then complete defect inspection through Machine Learning classifiers. Threshold segmentation and edge FE methods are commonly used [ 7 ]. Fast inspection speed is its biggest advantage.…”

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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“…Various NDT methods have been presented in decades, such as the inspections based on laser scanning, eddy current measurement, magnetic leakage testing, and photoelectric detection [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Surface non-destructively inspection of annular ceramic metal coating based on data augmentation and deep learning

LIANG,

Ye,

Wang

et al. 2024

Preprint

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The defects in the metal coating surface of annular ceramic workpiece have significant effects on the conductivity and reliability. Due to the irregularity, small area, and few sample number of defects, it is difficult to achieve efficient and accurate inspection. This paper presents a defect inspection framework based on deep learning for the metal coating surface of annular ceramic workpiece. Firstly, an image acquisition system for the coating surface is designed, and the defects characteristics are analyzed. Then, a surface image data set is constructed through five data augmentation strategies in order to solve the problem of insufficient samples. Finally, a defect detection framework for ceramic metal coating surface based on improved YOLOv7 model is established. By optimizing the clustering algorithm of target box, introducing an attention mechanism, and improving the MPConv structure, the efficient and precise identification of different defects is realized. Experimental results show that the recognition rate of defects including scratch, deficiency, scuffing, and dot is higher than 94%, and the average detection time is about 30 ms. The proposed detection framework based on deep learning shows great application potential in the fields of precise coating and manufacturing of ceramic materials.

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“…Machine vision technology uses image acquisition equipment to obtain visual information about objects and perform analysis and processing ( Zhao et al, 2022 ; Sivaranjani & Senthilrani, 2023 ). It can achieve automated testing, improve production efficiency, and reduce production costs ( Dong et al, 2022 ). Research on bare PCB defect detection algorithms based on machine vision theory can provide new ideas for machine vision technology, promote technological progress in related fields, and help expand the application field of machine vision and defect detection technology to areas such as medical ( Coraci, Tognolo & Masiero, 2023 ; Gao et al, 2023 ), security ( Mallaiyan Sathiaseelan et al, 2021 ; Ge, Dan & Li, 2020 ), and smart home technology ( Yu & Pei, 2021 ; Talaat, Arafa & Metwally, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Defect identification of bare printed circuit boards based on Bayesian fusion of multi-scale features

Han,

Li,

Wang

et al. 2024

PeerJ Computer Science

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The aim of this article is to propose a defect identification method for bare printed circuit boards (PCB) based on multi-feature fusion. This article establishes a description method for various features of grayscale, texture, and deep semantics of bare PCB images. First, the multi-scale directional projection feature, the multi-scale grey scale co-occurrence matrix feature, and the multi-scale gradient directional information entropy feature of PCB were extracted to build the shallow features of defect images. Then, based on migration learning, the feature extraction network of the pre-trained Visual Geometry Group16 (VGG-16) convolutional neural network model was used to extract the deep semantic feature of the bare PCB images. A multi-feature fusion method based on principal component analysis and Bayesian theory was established. The shallow image feature was then fused with the deep semantic feature, which improved the ability of feature vectors to characterize defects. Finally, the feature vectors were input as feature sequences to support vector machines for training, which completed the classification and recognition of bare PCB defects. Experimental results show that the algorithm integrating deep features and multi-scale shallow features had a high recognition rate for bare PCB defects, with an accuracy rate of over 99%.

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Application of machine vision-based NDT technology in ceramic surface defect detection – a review

Cited by 9 publications

References 92 publications

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

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

Surface non-destructively inspection of annular ceramic metal coating based on data augmentation and deep learning

Defect identification of bare printed circuit boards based on Bayesian fusion of multi-scale features

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