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Attention‐optimized 3D segmentation and reconstruction system for sewer pipelines employing multi‐view images.

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Title: Attention‐optimized 3D segmentation and reconstruction system for sewer pipelines employing multi‐view images.
Authors: Ma, Duo1,2 (AUTHOR), Wang, Niannian1,2,3 (AUTHOR) wnnian@zzu.edu.cn, Fang, Hongyuan1,2,3 (AUTHOR), Chen, Weiwei4 (AUTHOR), Li, Bin1,2 (AUTHOR), Zhai, Kejie1,2 (AUTHOR)
Source: Computer-Aided Civil & Infrastructure Engineering. 2/17/2025, Vol. 40 Issue 5, p594-613. 20p.
Subjects: Sewer pipes, Image segmentation, Deep learning, Signal detection, Engineering inspection, Image reconstruction algorithms, Depth perception, Three-dimensional imaging
Abstract: Existing deep learning‐based defect inspection results on images lack depth information to fully demonstrate the sewer, despite their high accuracy. To address this limitation, a novel attention‐optimized three‐dimensional (3D) segmentation and reconstruction system for sewer pipelines is presented. First, a real‐time sewer segmentation method called AM‐Pipe‐SegNet is developed to inspect defects (i.e., misalignment, obstacle, and fracture) efficiently. Attention mechanisms (AMs) are introduced to improve the performance of segmentation. Second, an attention‐optimized and sparse‐initialized depth estimation network called AM‐Pipe‐DepNet is presented to generate depth maps from multi‐view images. Third, a 2D‐to‐3D mapping algorithm is proposed to remove noise and transform the sewer segmentation results into 3D spaces. Comparison experiments reveal that incorporating AMs into the network significantly enhances pipe segmentation and 3D reconstruction performance. Finally, two digital replicas of real sewer pipes are built based on photos taken by probes, providing valuable insights for sewer maintenance. [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.)
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DbLabel: Engineering Source
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PubTypeId: academicJournal
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  Data: Attention‐optimized 3D segmentation and reconstruction system for sewer pipelines employing multi‐view images.
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  Data: <searchLink fieldCode="AR" term="%22Ma%2C+Duo%22">Ma, Duo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Niannian%22">Wang, Niannian</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> wnnian@zzu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Fang%2C+Hongyuan%22">Fang, Hongyuan</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Weiwei%22">Chen, Weiwei</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Bin%22">Li, Bin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhai%2C+Kejie%22">Zhai, Kejie</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Computer-Aided+Civil+%26+Infrastructure+Engineering%22">Computer-Aided Civil & Infrastructure Engineering</searchLink>. 2/17/2025, Vol. 40 Issue 5, p594-613. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Sewer+pipes%22">Sewer pipes</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+detection%22">Signal detection</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+inspection%22">Engineering inspection</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction+algorithms%22">Image reconstruction algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Depth+perception%22">Depth perception</searchLink><br /><searchLink fieldCode="DE" term="%22Three-dimensional+imaging%22">Three-dimensional imaging</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Existing deep learning‐based defect inspection results on images lack depth information to fully demonstrate the sewer, despite their high accuracy. To address this limitation, a novel attention‐optimized three‐dimensional (3D) segmentation and reconstruction system for sewer pipelines is presented. First, a real‐time sewer segmentation method called AM‐Pipe‐SegNet is developed to inspect defects (i.e., misalignment, obstacle, and fracture) efficiently. Attention mechanisms (AMs) are introduced to improve the performance of segmentation. Second, an attention‐optimized and sparse‐initialized depth estimation network called AM‐Pipe‐DepNet is presented to generate depth maps from multi‐view images. Third, a 2D‐to‐3D mapping algorithm is proposed to remove noise and transform the sewer segmentation results into 3D spaces. Comparison experiments reveal that incorporating AMs into the network significantly enhances pipe segmentation and 3D reconstruction performance. Finally, two digital replicas of real sewer pipes are built based on photos taken by probes, providing valuable insights for sewer maintenance. [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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        Value: 10.1111/mice.13241
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        Text: English
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        Type: general
      – SubjectFull: Image segmentation
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Signal detection
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      – SubjectFull: Engineering inspection
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      – SubjectFull: Image reconstruction algorithms
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      – SubjectFull: Depth perception
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      – SubjectFull: Three-dimensional imaging
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      – TitleFull: Attention‐optimized 3D segmentation and reconstruction system for sewer pipelines employing multi‐view images.
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            NameFull: Ma, Duo
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            NameFull: Wang, Niannian
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              M: 02
              Text: 2/17/2025
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              Y: 2025
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