Multi‐stage detection of warped ceiling panel using ensemble vision models for automated localization and quantification.
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| Title: | Multi‐stage detection of warped ceiling panel using ensemble vision models for automated localization and quantification. |
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| Authors: | Guo, Qinghua1,2 (AUTHOR), Gao, Weihang1,2 (AUTHOR) weihang_gao@tongji.edu.cn, Kong, Qingzhao1,2 (AUTHOR), Lu, Xilin1,2 (AUTHOR) |
| Source: | Computer-Aided Civil & Infrastructure Engineering. 7/18/2025, Vol. 40 Issue 18, p2713-2728. 16p. |
| Subjects: | Hough transforms, Computer vision, Automatic tracking, Convolutional neural networks, Detection algorithms, Gaging |
| Abstract: | Suspended ceiling systems constitute a pivotal non‐structural component in buildings, and the warping of panels not only compromises the seismic performance but also affects the functional integrity. This paper proposes a novel multi‐stage warped panel detection (MWPD) method to automatically locate warped panels from two‐dimensional images and quantify their deformation. First, the Deep Hough Transform (DHT) is employed to localize the runner line, after that, each detected line is expanded to a rectangular strip. Then ResNet18 classifies the strips as warped or intact. Those classified as warped will undergo Gabor and horizontal Sobel filters successively to highlight the curved edge. Subsequently, the Generalized Hough Transform (GHT) is used to locate pixel points on the curve, and fitting these points yields the pixel‐level radius of curvature. Leveraging known orthogonal relationships and geometric dimensions of runners, pixel quantification is converted into physical maximum deflection. The experiments include two aspects: the first is conducted on a validation dataset to verify the localization stability, and the second is carried out on‐site for quantification validation. Results demonstrate that the proposed MWPD method effectively localizes the warped panel, achieving an accuracy of 92.2% on the validation dataset. Additionally, the quantitative test has achieved an accuracy of approximately 85%. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer-Aided Civil & Infrastructure Engineering is the property of Wiley-Blackwell 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: 186600922 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multi‐stage detection of warped ceiling panel using ensemble vision models for automated localization and quantification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Guo%2C+Qinghua%22">Guo, Qinghua</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Weihang%22">Gao, Weihang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> weihang_gao@tongji.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Kong%2C+Qingzhao%22">Kong, Qingzhao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Xilin%22">Lu, Xilin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer-Aided+Civil+%26+Infrastructure+Engineering%22">Computer-Aided Civil & Infrastructure Engineering</searchLink>. 7/18/2025, Vol. 40 Issue 18, p2713-2728. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hough+transforms%22">Hough transforms</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+tracking%22">Automatic tracking</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Detection+algorithms%22">Detection algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Gaging%22">Gaging</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Suspended ceiling systems constitute a pivotal non‐structural component in buildings, and the warping of panels not only compromises the seismic performance but also affects the functional integrity. This paper proposes a novel multi‐stage warped panel detection (MWPD) method to automatically locate warped panels from two‐dimensional images and quantify their deformation. First, the Deep Hough Transform (DHT) is employed to localize the runner line, after that, each detected line is expanded to a rectangular strip. Then ResNet18 classifies the strips as warped or intact. Those classified as warped will undergo Gabor and horizontal Sobel filters successively to highlight the curved edge. Subsequently, the Generalized Hough Transform (GHT) is used to locate pixel points on the curve, and fitting these points yields the pixel‐level radius of curvature. Leveraging known orthogonal relationships and geometric dimensions of runners, pixel quantification is converted into physical maximum deflection. The experiments include two aspects: the first is conducted on a validation dataset to verify the localization stability, and the second is carried out on‐site for quantification validation. Results demonstrate that the proposed MWPD method effectively localizes the warped panel, achieving an accuracy of 92.2% on the validation dataset. Additionally, the quantitative test has achieved an accuracy of approximately 85%. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer-Aided Civil & Infrastructure Engineering is the property of Wiley-Blackwell 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: 10.1111/mice.13414 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 2713 Subjects: – SubjectFull: Hough transforms Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Automatic tracking Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Detection algorithms Type: general – SubjectFull: Gaging Type: general Titles: – TitleFull: Multi‐stage detection of warped ceiling panel using ensemble vision models for automated localization and quantification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Guo, Qinghua – PersonEntity: Name: NameFull: Gao, Weihang – PersonEntity: Name: NameFull: Kong, Qingzhao – PersonEntity: Name: NameFull: Lu, Xilin IsPartOfRelationships: – BibEntity: Dates: – D: 18 M: 07 Text: 7/18/2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10939687 Numbering: – Type: volume Value: 40 – Type: issue Value: 18 Titles: – TitleFull: Computer-Aided Civil & Infrastructure Engineering Type: main |
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