Dense detection algorithm for ceramic tile defects based on improved YOLOv8.
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| Title: | Dense detection algorithm for ceramic tile defects based on improved YOLOv8. |
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| Authors: | Yu, Mei1,2 (AUTHOR), Li, Yuxin1 (AUTHOR), Li, Zhilin1 (AUTHOR), Yan, Peng1 (AUTHOR), Li, Xiutong1 (AUTHOR), Tian, Qin3 (AUTHOR), Xie, Benliang1,2,4 (AUTHOR) blxie@gzu.edu.cn |
| Source: | Journal of Intelligent Manufacturing. Dec2025, Vol. 36 Issue 8, p5613-5628. 16p. |
| Subjects: | Ceramic tiles, Deep learning, Artificial neural networks, Inspection & review, Object recognition (Computer vision), Defect tracking (Computer software development), Machine learning |
| Abstract: | As a common building decoration material, ceramic tiles have been widely used in modern society, and deep learning inspection methods are increasingly employed for tile quality inspection. However, current methods face issues such as slow detection velocity and diminished precision in ceramic tiles detection. To resolve these issues, this study presents a dense detection algorithm for ceramic tile defects with an improved YOLOv8. The model redesigns the CSPLayer (Cross Stage Partial Layer) structure by incorporating the BiFormer architecture, and the SCConv (Spatial and Channel Reconstruction Convolution) is employed to replace the ordinary convolution in the Neck and Head. Furthermore, the MPDIoU + DFL (Distribution Focal Loss) is adopted as the bounding box regression loss function, and the EMA (Efficient Multi-Scale Attention mechanism) attention module is introduced to improve the significance and precision of the defective feature information detection. Experimental results indicate that the final improved model has a size of 58.6 MB, the mAP@0.5 reaches 95.62%, and the FPS is 145.4. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 189056613 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dense detection algorithm for ceramic tile defects based on improved YOLOv8. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yu%2C+Mei%22">Yu, Mei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Yuxin%22">Li, Yuxin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Zhilin%22">Li, Zhilin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yan%2C+Peng%22">Yan, Peng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Xiutong%22">Li, Xiutong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tian%2C+Qin%22">Tian, Qin</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xie%2C+Benliang%22">Xie, Benliang</searchLink><relatesTo>1,2,4</relatesTo> (AUTHOR)<i> blxie@gzu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+Manufacturing%22">Journal of Intelligent Manufacturing</searchLink>. Dec2025, Vol. 36 Issue 8, p5613-5628. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Ceramic+tiles%22">Ceramic tiles</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Inspection+%26+review%22">Inspection & review</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Defect+tracking+%28Computer+software+development%29%22">Defect tracking (Computer software development)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: As a common building decoration material, ceramic tiles have been widely used in modern society, and deep learning inspection methods are increasingly employed for tile quality inspection. However, current methods face issues such as slow detection velocity and diminished precision in ceramic tiles detection. To resolve these issues, this study presents a dense detection algorithm for ceramic tile defects with an improved YOLOv8. The model redesigns the CSPLayer (Cross Stage Partial Layer) structure by incorporating the BiFormer architecture, and the SCConv (Spatial and Channel Reconstruction Convolution) is employed to replace the ordinary convolution in the Neck and Head. Furthermore, the MPDIoU + DFL (Distribution Focal Loss) is adopted as the bounding box regression loss function, and the EMA (Efficient Multi-Scale Attention mechanism) attention module is introduced to improve the significance and precision of the defective feature information detection. Experimental results indicate that the final improved model has a size of 58.6 MB, the mAP@0.5 reaches 95.62%, and the FPS is 145.4. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10845-024-02523-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 5613 Subjects: – SubjectFull: Ceramic tiles Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Inspection & review Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Defect tracking (Computer software development) Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Dense detection algorithm for ceramic tile defects based on improved YOLOv8. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yu, Mei – PersonEntity: Name: NameFull: Li, Yuxin – PersonEntity: Name: NameFull: Li, Zhilin – PersonEntity: Name: NameFull: Yan, Peng – PersonEntity: Name: NameFull: Li, Xiutong – PersonEntity: Name: NameFull: Tian, Qin – PersonEntity: Name: NameFull: Xie, Benliang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09565515 Numbering: – Type: volume Value: 36 – Type: issue Value: 8 Titles: – TitleFull: Journal of Intelligent Manufacturing Type: main |
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