RGB and Thermal Image Analysis for Marble Crack Detection with Deep Learning-Reference-Cited by-同舟云学术

RGB and Thermal Image Analysis for Marble Crack Detection with Deep Learning

Published:2023 Issue: Volume: Page:427-438
ISSN:2524-7565
Container-title:Algorithms for Intelligent Systems
language:
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Author:

Vrochidou Eleni^ORCID,Sidiropoulos George K.^ORCID,Ouzounis Athanasios G.^ORCID,Tsimperidis Ioannis^ORCID,Sarafis Ilias T.,Kalpakis Vassilis,Stamkos Andreas,Papakostas George A.^ORCID

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-4626-6_36

Reference26 articles.

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2. Ren Z, Fang F, Yan N, Wu Y (2022) State of the art in defect detection based on machine vision. Int J Precis Eng Manuf Technol 9:661–691. https://doi.org/10.1007/s40684-021-00343-6

3. Karaali İ, Eminağaoğlu M (2020) A convolutional neural network model for marble quality classification. SN Appl Sci 2:1733. https://doi.org/10.1007/s42452-020-03520-5

4. Ouzounis A, Sidiropoulos G, Papakostas G, Sarafis I, Stamkos A, Solakis G (2021) Interpretable deep learning for marble tiles sorting. In: Proceedings of the 2nd international conference on deep learning theory and applications. SCITEPRESS—Science and Technology Publications, pp 101–108. https://doi.org/10.5220/0010517001010108

5. Vrochidou E, Sidiropoulos GK, Ouzounis AG, Lampoglou A, Tsimperidis I, Papakostas GA, Sarafis IT, Kalpakis V, Stamkos A (2022) Towards robotic marble resin application: crack detection on marble using deep learning. Electronics 11:3289. https://doi.org/10.3390/electronics11203289