Zum Hauptinhalt springen
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Non-destructive detection for mosaic ceramic surface defects based on convolutional neural networks

  • Dr. Guanping Dong received his Ph.D. degree in South China University of Technology, P. R. China. His research interests include machine vision, image recognition, defect detection, and visual image feature extraction.

    ORCID logo EMAIL logo
    ,

    Shanwei Sun is a Master’s students at the Jingdezhen Ceramic University, Jingdezhen, P. R. China.

    ,

    Xiangyu Kong is a teacher of Guangdong Polytechnic of Science and Trade, Guangzhou, P. R. China. His research interests include smart manufacturing, defect detection technology.

    ,

    Nanshou Wu is a Ph.D. student at South China Normal University, Guangzhou, P. R. China, researching on biophotonic imaging technology.

    ,

    Hong Zhang is an undergraduate student at the Jingdezhen Ceramic University, Jingdezhen, P. R. China.

    ,

    Xiangyang Chen is a teacher of Guangdong University of Technology, Guangzhou, P. R. China. His research interests include robot trajectory optimization and visual detection.

    ,

    Dr. Hao Feng is a Professor of Jingdezhen Ceramic University, Jingdezhen, P. R. China. His research interests include intelligent manufacturing, ceramic defect detection, and energy saving technology.

    ,

    Pingnan Huang is a Ph.D. student at South China University of Technology, Guangzhou, P. R. China. His research interests focus on the design and optimization of micro-reaction systems.

    und

    Dr. Zixi Wang is a Professor in the Department of Mechanical Engineering, Tsinghua University, P. R. China. His research includes magnetic suspension bearing and magnetic drive technology, air floating bearing technology, and sealing technology.

Veröffentlicht/Copyright: 11. August 2023
Veröffentlichen auch Sie bei De Gruyter Brill

Abstract

Mosaic ceramic art pattern with noble, elegant features, it is a unique form of art creation in ancient Greece and the ancient Rome period has been loved by artists and created a lot of classic large-scale exterior mosaic ceramic art works. Small size square mosaic ceramic as the basic raw material for the creation of large exterior mosaic art, it directly affects the quality of the work created by the artist, so these ceramic mosaic ceramic materials need to undergo rigorous inspection to meet the needs of the artist’s high-quality art creation. However, small size multi-color square mosaic ceramics are different from ordinary large target ceramics, they have the characteristics of small size and easy reflection, currently mainly using manual inspection, the existing automatic inspection methods have the problem of low efficiency and accuracy, cannot meet the needs of artists for the quantity and quality of mosaic ceramics. To solve these problems, this paper proposes a new convolutional network-based fast nondestructive testing method for detecting square mosaic tiles. The detection method is based on the convolutional neural network YOLOv5s model, and by introducing the AF-FPN module and the data enhancement module, the method further improves the recognition performance of the model relative to the original YOLOv5s model and achieves the fast detection of surface defects on square mosaic ceramics. The experimental results show that the detection method for small size multicolor square mosaic ceramic tile surface minor defects detection rate of up to 94 % or more, a single square mosaic ceramic detection time of 0.41 s. The method takes into account the detection accuracy and speed, can be fast and accurate screening of high-quality, defect-free small size multicolor square mosaic ceramic, to meet the artist’s requirements for high-quality mosaic ceramic raw materials Quality and quantity requirements, to ensure the quality of the creation of mosaic art patterns, to better show the charm of the mosaic art patterns role. At the same time, the method can not only be applied to the detection of mosaic ceramics, the method can also be applied to have a similar small volume, easy to reflect the characteristics of small target object defect detection.


Corresponding authors: Guanping Dong, Jingdezhen Ceramic University, Jingdezhen, China, E-mail: ; and Zixi Wang, State Key Laboratory of Tribology, Tsinghua University Department of Mechanical Engineering, Beijing, China, E-mail:

Funding source: The Fund of Science and Technology Program of Jingdezhen City

Award Identifier / Grant number: 20212GYZD009-19

Funding source: The Youth Fund of Jiangxi Provincial Department of Education

Award Identifier / Grant number: 72005256

Funding source: The PhD Research Start-up Fund of Jingdezhen Ceramic University

Award Identifier / Grant number: 20298002

Funding source: The Fund of Jiangxi Provincial Department of Education

Award Identifier / Grant number: GJJ2201020

Funding source: The National Key R&D Program of China

Award Identifier / Grant number: 2021YFB4001500

About the authors

Guanping Dong

Dr. Guanping Dong received his Ph.D. degree in South China University of Technology, P. R. China. His research interests include machine vision, image recognition, defect detection, and visual image feature extraction.

Shanwei Sun

Shanwei Sun is a Master’s students at the Jingdezhen Ceramic University, Jingdezhen, P. R. China.

Xiangyu Kong

Xiangyu Kong is a teacher of Guangdong Polytechnic of Science and Trade, Guangzhou, P. R. China. His research interests include smart manufacturing, defect detection technology.

Nanshou Wu

Nanshou Wu is a Ph.D. student at South China Normal University, Guangzhou, P. R. China, researching on biophotonic imaging technology.

Hong Zhang

Hong Zhang is an undergraduate student at the Jingdezhen Ceramic University, Jingdezhen, P. R. China.

Xiangyang Chen

Xiangyang Chen is a teacher of Guangdong University of Technology, Guangzhou, P. R. China. His research interests include robot trajectory optimization and visual detection.

Hao Feng

Dr. Hao Feng is a Professor of Jingdezhen Ceramic University, Jingdezhen, P. R. China. His research interests include intelligent manufacturing, ceramic defect detection, and energy saving technology.

Pingnan Huang

Pingnan Huang is a Ph.D. student at South China University of Technology, Guangzhou, P. R. China. His research interests focus on the design and optimization of micro-reaction systems.

Zixi Wang

Dr. Zixi Wang is a Professor in the Department of Mechanical Engineering, Tsinghua University, P. R. China. His research includes magnetic suspension bearing and magnetic drive technology, air floating bearing technology, and sealing technology.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This research was supported by the National Key R&D Program of China, Project No. 2021YFB4001500, and the PhD Research Start-up Fund of Jingdezhen Ceramic University, Project No. 20298002, and the Youth Fund of Jiangxi Provincial Department of Education, Project No. 72005256, and the Fund of Jiangxi Provincial Department of Education, Project No. GJJ2201020, and the Fund of Science and Technology Program of Jingdezhen City, Project No. 20212GYZD009-19.

  3. Conflict of interest statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] A. Oral and E. Inal, “Marble mosaic tiling automation with a four degrees of freedom cartesian robot,” Robot. Comput. Integrated Manuf., vol. 25, pp. 589–596, 2009, https://doi.org/10.1016/j.rcim.2008.04.003.Suche in Google Scholar

[2] I. Cayiroglu and B. Demir, “Computer assisted glass mosaic tiling automation,” Robot. Comput. Integrated Manuf., vol. 28, pp. 583–591, 2012, https://doi.org/10.1016/j.rcim.2012.02.008.Suche in Google Scholar

[3] G. Dong, S. Sun, N. Wu, H. Feng, P. Huang, and M. Pan, “Research on automatic mosaic ceramic tiling method based on color matching,” Ceram. Int., vol. 47, pp. 31451–31456, 2021, https://doi.org/10.1016/j.ceramint.2021.08.021.Suche in Google Scholar

[4] P. Pan, Y. Xu, C. Xing, and Y. Chen, “Crack detection for nuclear containments based on multi-feature fused semantic segmentation,” Construct. Build. Mater., vol. 329, p. 127137, 2022, https://doi.org/10.1016/j.conbuildmat.2022.127137.Suche in Google Scholar

[5] S. Punj, J. Singh, and K. Singh, “Ceramic biomaterials: properties, state of the art and future prospectives,” Ceram. Int., vol. 47, pp. 28059–28074, 2021, https://doi.org/10.1016/j.ceramint.2021.06.238.Suche in Google Scholar

[6] P. Awoyera, O. Olalusi, and N. Iweriebo, “Physical, strength, and microscale properties of plastic fiber-reinforced concrete containing fine ceramics particles,” Materialia, vol. 15, p. 100970, 2021, https://doi.org/10.1016/j.mtla.2020.100970.Suche in Google Scholar

[7] J. Schwarzmann and T. Beiküfner, “Online magnetic flux leakage detection of inclusions and inhomogeneities in cold rolled steel plate,” Mater. Test., vol. 64, pp. 1512–1526, 2022, https://doi.org/10.1515/mt-2022-0182.Suche in Google Scholar

[8] M. Siebenhofer, F. Baiutti, J. Sirvent, et al.., “Exploring point defects and trap states in undoped SrTiO3 single crystals,” J. Eur. Ceram. Soc., vol. 42, pp. 1510–1521, 2022, https://doi.org/10.1016/j.jeurceramsoc.2021.10.010.Suche in Google Scholar

[9] M. Gollnick, P. Giese, D. Hein, G. Meschut, and D. Herfert, “Early stage crack detection in mechanically joined steel/aluminum joints by condition monitoring,” Mater. Test., vol. 62, pp. 877–882, 2020, https://doi.org/10.3139/120.111558.Suche in Google Scholar

[10] G. Rosati, G. Boschetti, A. Biondi, and A. Rossi, “Real-time defect detection on highly reflective curved surfaces,” Opt Laser. Eng., vol. 47, pp. 379–384, 2009, https://doi.org/10.1016/j.optlaseng.2008.03.010.Suche in Google Scholar

[11] K. Silva, G. Almeida, C. Nunes, G. R. Pereira, D. Kadoke, and W. Daum, “Automation of pipe defect detection and characterization by structured light,” Mater. Test., vol. 63, pp. 55–61, 2021, https://doi.org/10.1515/mt-2020-0008.Suche in Google Scholar

[12] V. Sindagi and S. Srivastava, “Domain adaptation for automatic OLED panel defect detection using adaptive support vector data description,” Int. J. Comput. Vis., vol. 122, pp. 193–211, 2017, https://doi.org/10.1007/s11263-016-0953-y.Suche in Google Scholar

[13] G. Dong, S. Sun, N. Wu, X. Chen, P. Huang, and Z. Wang, “A rapid detection method for the surface defects of mosaic ceramic tiles,” Ceram. Int., vol. 48, pp. 15462–15469, 2022, https://doi.org/10.1016/j.ceramint.2022.02.080.Suche in Google Scholar

[14] C. Liu, N. Jeyaprakash, and C. Yang, “Material characterization and defect detection of additively manufactured ceramic teeth using non-destructive techniques,” Ceram. Int., vol. 47, pp. 7017–7031, 2021, https://doi.org/10.1016/j.ceramint.2020.11.052.Suche in Google Scholar

[15] X. Zabulis, M. Papara, A. Chatziargyriou, and T. D. Karapantsios, “Detection of densely dispersed spherical bubbles in digital images based on a template matching technique. Application to wet foams,” Colloids Surf. A Physicochem. Eng. Asp., vol. 309, pp. 96–106, 2007, https://doi.org/10.1016/j.colsurfa.2007.01.007.Suche in Google Scholar

[16] X. Li, Y. Gao, P. Ge, L. Zhang, W. Bi, and J. Meng, “Nucleation location and propagation direction of radial and median cracks for brittle material in scratching,” Ceram. Int., vol. 45, pp. 7524–7536, 2019, https://doi.org/10.1016/j.ceramint.2019.01.046.Suche in Google Scholar

[17] T. Czimmermann, G. Ciuti, M. Milazzo, et al.., “Visual-based defect detection and classification approaches for industrial applications—a survey,” Sensors, vol. 20, pp. 1–25, 2020, https://doi.org/10.3390/s20051459.Suche in Google Scholar PubMed PubMed Central

[18] G. Dong, S. Sun, Z. Wang, et al.., “Application of machine vision-based NDT technology in ceramic surface defect detection – a review,” Mater. Test., vol. 64, pp. 202–219, 2022, https://doi.org/10.1515/mt-2021-2012.Suche in Google Scholar

[19] J. Zhang, H. Wang, Y. Tian, and K. Liu, “An accurate fuzzy measure-based detection method for various types of defects on strip steel surfaces,” Comput. Ind., vol. 122, pp. 103231–103243, 2020, https://doi.org/10.1016/j.compind.2020.103231.Suche in Google Scholar

[20] J. Wang, P. Fu, and R. X. Gao, “Machine vision intelligence for product defect inspection based on deep learning and Hough transform,” J. Manuf. Syst., vol. 51, pp. 52–60, 2019, https://doi.org/10.1016/j.jmsy.2019.03.002.Suche in Google Scholar

[21] P. Bhatt, R. Malhan, P. Rajendran, et al.., “Image-based surface defect detection using deep learning: a review,” J. Comput. Inf. Sci. Eng., vol. 21, pp. 1–23, 2021, https://doi.org/10.1115/1.4049535.Suche in Google Scholar

[22] K. Yao, A. Ortiz, and F. Bonnin-Pascual, “A DCNN-based arbitrarily-oriented object detector with application to quality control and inspection,” Comput. Ind., vol. 142, p. 103737, 2022, https://doi.org/10.1016/j.compind.2022.103737.Suche in Google Scholar

[23] N. Ahamad and J. B. Rao, “Analysis and detection of surface defects in ceramic tile using image processing techniques,” Springer Ind., vol. 372, pp. 575–582, 2016, https://doi.org/10.1007/978-81-322-2728-1_54.Suche in Google Scholar

[24] H. Elbehiery, A. Hefnawy, and M. Elewa, “Surface defects detection for ceramic tiles using image processing and morphological techniques,” Proc.World Acad. Sci. Eng. Technol., vol. 5, pp. 158–162, 2005, https://doi.org/10.5281/zenodo.1084534.Suche in Google Scholar

[25] S. Emam and S. Sayyedbarzani, “Dimensional deviation measurement of ceramic tiles according to ISO 10545-2 using the machine vision,” Int. J. Adv. Manuf. Technol., vol. 100, pp. 1405–1418, 2019, https://doi.org/10.1007/s00170-018-2781-4.Suche in Google Scholar

[26] A. Sioma, “Automated control of surface defects on ceramic tiles using 3D image analysis,” Materials, vol. 13, p. 1250, 2020, https://doi.org/10.3390/ma13051250.Suche in Google Scholar PubMed PubMed Central

[27] S. Guan, “Fabric defect delaminating detection based on visual saliency in HSV color space,” J. Text. Inst., vol. 109, pp. 1560–1573, 2018, https://doi.org/10.1080/00405000.2018.1434112.Suche in Google Scholar

[28] X. Han, Z. Zhao, L. Chen, et al.., “Structural damage-causing concrete cracking detection based on a deep-learning method,” Construct. Build. Mater., vol. 337, p. 127562, 2022, https://doi.org/10.1016/j.conbuildmat.2022.127562.Suche in Google Scholar

[29] Z. Zhao, “Review of non-destructive testing methods for defect detection of ceramics,” Ceram. Int., vol. 47, pp. 4389–4397, 2021, https://doi.org/10.1016/j.ceramint.2020.10.065.Suche in Google Scholar

[30] N. Kheradmandi and V. Mehranfar, “A critical review and comparative study on image segmentation-based techniques for pavement crack detection,” Construct. Build. Mater., vol. 321, pp. 1–26, 2022, https://doi.org/10.1016/j.conbuildmat.2021.126162.Suche in Google Scholar

[31] Y. Du, N. Pan, Z. Xu, F. Deng, Y. Shen, and H. Kang, “Pavement distress detection and classification based on YOLO network,” Int. J. Pavement Eng., vol. 22, pp. 1659–1672, 2021, https://doi.org/10.1080/10298436.2020.1714047.Suche in Google Scholar

Published Online: 2023-08-11
Published in Print: 2023-09-26

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 27.4.2026 von https://www.degruyterbrill.com/document/doi/10.1515/mt-2023-0051/html?lang=de
Button zum nach oben scrollen