Multi-modal cascade detection of pipeline defects based on deep transfer metric learning.
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| Title: | Multi-modal cascade detection of pipeline defects based on deep transfer metric learning. |
|---|---|
| Authors: | Gao, Boxuan1 (AUTHOR), Zhao, Hong1 (AUTHOR) hzhao_cn@163.com, Miao, Xingyuan1 (AUTHOR) |
| Source: | Engineering Failure Analysis. Jun2024, Vol. 160, pN.PAG-N.PAG. 1p. |
| Subjects: | Magnetic flux leakage, Transfer of training, Pipeline maintenance & repair, Computer vision, Pipeline transportation |
| Abstract: | • A multi-modal cascade detection framework is proposed to detect pipeline defects. • MFL detection device is designed to collect three-axis MFL signals of defects. • A DTML method is used to solve the similarity of defect appearance. • Multiple ResNet101 networks are designed to estimate defect size. Pipeline defect detection technology plays an important role in pipeline maintenance and transportation. Defect detection based on machine learning methods has gained considerable attention in practical engineering. However, it is still challenging to provide an accurate diagnosis and defect size estimation due to the poor inter-class discriminability and intra-class concentration. Such as, it is difficult to distinguish the hole defect, which is similar in appearance to dent defect. For this purpose, a multi-modal cascade detection framework of pipeline defects based on Deep Transfer Metric Learning (DTML) is proposed for defect recognition and defect size estimation, which integrates with machine vision and Magnetic Flux Leakage (MFL). DTML model based on ResNet50 is designed to extract discriminative features from defect images obtained through vision sensor. To enhance the features of MFL signals, Gramian Angular Field (GAF) is used to achieve the two-dimensional feature extraction. After that, three ResNet101 models are developed to estimate the pipeline defect size of different types. The experimental results demonstrate that the proposed multi-modal cascade detection framework performs well in defect recognition and defect size estimation. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Failure Analysis is the property of Pergamon Press - An Imprint of Elsevier Science 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 177085948 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multi-modal cascade detection of pipeline defects based on deep transfer metric learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gao%2C+Boxuan%22">Gao, Boxuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Hong%22">Zhao, Hong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hzhao_cn@163.com</i><br /><searchLink fieldCode="AR" term="%22Miao%2C+Xingyuan%22">Miao, Xingyuan</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Failure+Analysis%22">Engineering Failure Analysis</searchLink>. Jun2024, Vol. 160, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Magnetic+flux+leakage%22">Magnetic flux leakage</searchLink><br /><searchLink fieldCode="DE" term="%22Transfer+of+training%22">Transfer of training</searchLink><br /><searchLink fieldCode="DE" term="%22Pipeline+maintenance+%26+repair%22">Pipeline maintenance & repair</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Pipeline+transportation%22">Pipeline transportation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: • A multi-modal cascade detection framework is proposed to detect pipeline defects. • MFL detection device is designed to collect three-axis MFL signals of defects. • A DTML method is used to solve the similarity of defect appearance. • Multiple ResNet101 networks are designed to estimate defect size. Pipeline defect detection technology plays an important role in pipeline maintenance and transportation. Defect detection based on machine learning methods has gained considerable attention in practical engineering. However, it is still challenging to provide an accurate diagnosis and defect size estimation due to the poor inter-class discriminability and intra-class concentration. Such as, it is difficult to distinguish the hole defect, which is similar in appearance to dent defect. For this purpose, a multi-modal cascade detection framework of pipeline defects based on Deep Transfer Metric Learning (DTML) is proposed for defect recognition and defect size estimation, which integrates with machine vision and Magnetic Flux Leakage (MFL). DTML model based on ResNet50 is designed to extract discriminative features from defect images obtained through vision sensor. To enhance the features of MFL signals, Gramian Angular Field (GAF) is used to achieve the two-dimensional feature extraction. After that, three ResNet101 models are developed to estimate the pipeline defect size of different types. The experimental results demonstrate that the proposed multi-modal cascade detection framework performs well in defect recognition and defect size estimation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Failure Analysis is the property of Pergamon Press - An Imprint of Elsevier Science 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: 10.1016/j.engfailanal.2024.108216 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Magnetic flux leakage Type: general – SubjectFull: Transfer of training Type: general – SubjectFull: Pipeline maintenance & repair Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Pipeline transportation Type: general Titles: – TitleFull: Multi-modal cascade detection of pipeline defects based on deep transfer metric learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gao, Boxuan – PersonEntity: Name: NameFull: Zhao, Hong – PersonEntity: Name: NameFull: Miao, Xingyuan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 13506307 Numbering: – Type: volume Value: 160 Titles: – TitleFull: Engineering Failure Analysis Type: main |
| ResultId | 1 |
