Color image segmentation based multilevel thresholding for detection of cracks and bug-holes in concrete surface images using optimization methods.
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| Title: | Color image segmentation based multilevel thresholding for detection of cracks and bug-holes in concrete surface images using optimization methods. |
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| Authors: | Asgarinejad, Mahdi1 (AUTHOR), Bitaraf, Maryam1 (AUTHOR) maryam.bitaraf@ut.ac.ir, Ramezanianpour, Amir Mohammad1 (AUTHOR) |
| Source: | Advances in Structural Engineering. Jul2026, Vol. 29 Issue 10, p1973-1996. 24p. |
| Subjects: | Thresholding algorithms, Mathematical optimization, Structural health monitoring, Machine learning, Image segmentation |
| Abstract: | Automation in crack and bug-hole detection through non-destructive evaluation methods remains a major challenge in Structural Health Monitoring. Typically, machine learning models are fed with original, grayscale, or binary formats of concrete images. Original images retain excessive color data, grayscale formats reduce color without preserving structural relevance, and binary images often lack sufficient detail for assessing damage type and severity. To overcome these limitations, multilevel colored image thresholding using meta-heuristic algorithms such as Particle Swarm Optimization, Genetic Algorithm, Jaya and Sine Cosine Algorithm—guided by Otsu's objective function. This technique enhances the contrast, edges, and texture boundaries, which serve as crucial primitives for object recognition, compared to original images. Additionally, it increases pixel connectivity, thereby simplifying image analysis for deep learning and machine learning applications. In fact, this method reduces the number of colors by an average of 97.3%, significantly decreasing computational load, while maintaining a high Structural Similarity Index (SSIM) of 0.873. Experimental results demonstrate that the optimized images outperform the original, grayscale, and binary formats in both object detection performance and precise evaluation of damage severity and progression. This fusion of optimization and perceptual image representation presents a promising advancement for automated structural damage assessment. [ABSTRACT FROM AUTHOR] |
| Copyright of Advances in Structural Engineering is the property of Sage Publications Inc. 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: 194727731 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Color image segmentation based multilevel thresholding for detection of cracks and bug-holes in concrete surface images using optimization methods. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Asgarinejad%2C+Mahdi%22">Asgarinejad, Mahdi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bitaraf%2C+Maryam%22">Bitaraf, Maryam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> maryam.bitaraf@ut.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Ramezanianpour%2C+Amir+Mohammad%22">Ramezanianpour, Amir Mohammad</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Advances+in+Structural+Engineering%22">Advances in Structural Engineering</searchLink>. Jul2026, Vol. 29 Issue 10, p1973-1996. 24p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Thresholding+algorithms%22">Thresholding algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+health+monitoring%22">Structural health monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Automation in crack and bug-hole detection through non-destructive evaluation methods remains a major challenge in Structural Health Monitoring. Typically, machine learning models are fed with original, grayscale, or binary formats of concrete images. Original images retain excessive color data, grayscale formats reduce color without preserving structural relevance, and binary images often lack sufficient detail for assessing damage type and severity. To overcome these limitations, multilevel colored image thresholding using meta-heuristic algorithms such as Particle Swarm Optimization, Genetic Algorithm, Jaya and Sine Cosine Algorithm—guided by Otsu's objective function. This technique enhances the contrast, edges, and texture boundaries, which serve as crucial primitives for object recognition, compared to original images. Additionally, it increases pixel connectivity, thereby simplifying image analysis for deep learning and machine learning applications. In fact, this method reduces the number of colors by an average of 97.3%, significantly decreasing computational load, while maintaining a high Structural Similarity Index (SSIM) of 0.873. Experimental results demonstrate that the optimized images outperform the original, grayscale, and binary formats in both object detection performance and precise evaluation of damage severity and progression. This fusion of optimization and perceptual image representation presents a promising advancement for automated structural damage assessment. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Advances in Structural Engineering is the property of Sage Publications Inc. 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194727731 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/13694332251375204 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 1973 Subjects: – SubjectFull: Thresholding algorithms Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Structural health monitoring Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Image segmentation Type: general Titles: – TitleFull: Color image segmentation based multilevel thresholding for detection of cracks and bug-holes in concrete surface images using optimization methods. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Asgarinejad, Mahdi – PersonEntity: Name: NameFull: Bitaraf, Maryam – PersonEntity: Name: NameFull: Ramezanianpour, Amir Mohammad IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13694332 Numbering: – Type: volume Value: 29 – Type: issue Value: 10 Titles: – TitleFull: Advances in Structural Engineering Type: main |
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