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A novel multi-model cascade framework for pipeline defects detection based on machine vision.

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Title: A novel multi-model cascade framework for pipeline defects detection based on machine vision.
Authors: Gao, Boxuan1 (AUTHOR), Zhao, Hong1 (AUTHOR) hzhao_cn@163.com, Miao, Xingyuan1 (AUTHOR)
Source: Measurement (02632241). Oct2023, Vol. 220, pN.PAG-N.PAG. 1p.
Subjects: Computer vision, Generative adversarial networks, Pipeline transportation, Imaging systems, Data distribution
Abstract: • An image acquisition system is designed to capture pipeline surface images; • A SRGAN modified with a self-attention mechanism is used to enhance image quality; • An IVGG16 classifier is improved to effectively enhance the detection performance; • A Mask R-CNN model is developed to locate defects in panorama images. Defect detection technology is vital for ensuring the safety of pipelines during transportation. However, the current methods for defect detection using machine vision rely on having enough labeled defect samples. Unfortunately, some specific defect samples are difficult to obtain in engineering practice, which creates an imbalanced data problem and limits detection performance. Furthermore, traditional methods struggle to achieve satisfactory results with low-quality images. To solve these problems, a novel multi-model cascade framework based on machine vision is proposed. This framework uses a modified Super-Resolution Generative Adversarial Network (MSRGAN) with a self-attention mechanism to generate high-quality fake defect samples to balance data distribution. An improved Visual Geometry Group network (IVGG16) is also designed to enhance the performance of imbalanced defect classification, and Mask R-CNN is utilized to locate the defects. The experimental results demonstrate that the proposed framework performs well in recognizing imbalanced and low-quality samples, and it outperforms other state-of-the-art methods in terms of detection accuracy. [ABSTRACT FROM AUTHOR]
Copyright of Measurement (02632241) is the property of Elsevier B.V. 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: 171587113
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: A novel multi-model cascade framework for pipeline defects detection based on machine vision.
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  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)
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  Data: <searchLink fieldCode="JN" term="%22Measurement+%2802632241%29%22">Measurement (02632241)</searchLink>. Oct2023, Vol. 220, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Generative+adversarial+networks%22">Generative adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22Pipeline+transportation%22">Pipeline transportation</searchLink><br /><searchLink fieldCode="DE" term="%22Imaging+systems%22">Imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Data+distribution%22">Data distribution</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: • An image acquisition system is designed to capture pipeline surface images; • A SRGAN modified with a self-attention mechanism is used to enhance image quality; • An IVGG16 classifier is improved to effectively enhance the detection performance; • A Mask R-CNN model is developed to locate defects in panorama images. Defect detection technology is vital for ensuring the safety of pipelines during transportation. However, the current methods for defect detection using machine vision rely on having enough labeled defect samples. Unfortunately, some specific defect samples are difficult to obtain in engineering practice, which creates an imbalanced data problem and limits detection performance. Furthermore, traditional methods struggle to achieve satisfactory results with low-quality images. To solve these problems, a novel multi-model cascade framework based on machine vision is proposed. This framework uses a modified Super-Resolution Generative Adversarial Network (MSRGAN) with a self-attention mechanism to generate high-quality fake defect samples to balance data distribution. An improved Visual Geometry Group network (IVGG16) is also designed to enhance the performance of imbalanced defect classification, and Mask R-CNN is utilized to locate the defects. The experimental results demonstrate that the proposed framework performs well in recognizing imbalanced and low-quality samples, and it outperforms other state-of-the-art methods in terms of detection accuracy. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Measurement (02632241) is the property of Elsevier B.V. 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:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.measurement.2023.113374
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Computer vision
        Type: general
      – SubjectFull: Generative adversarial networks
        Type: general
      – SubjectFull: Pipeline transportation
        Type: general
      – SubjectFull: Imaging systems
        Type: general
      – SubjectFull: Data distribution
        Type: general
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      – TitleFull: A novel multi-model cascade framework for pipeline defects detection based on machine vision.
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            NameFull: Gao, Boxuan
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            NameFull: Zhao, Hong
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            NameFull: Miao, Xingyuan
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          Dates:
            – D: 01
              M: 10
              Text: Oct2023
              Type: published
              Y: 2023
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              Value: 220
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