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A graph attention reasoning model for prefabricated component detection.

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Title: A graph attention reasoning model for prefabricated component detection.
Authors: Zhou, Manxu1,2 (AUTHOR), Ye, Guanting3,4 (AUTHOR), Yuen, Ka‐Veng3,4 (AUTHOR) kvyuen@um.edu.mo, Yu, Wenhao5 (AUTHOR), Jin, Qiang1,2 (AUTHOR) tm-jinq@xjau.edu.cn
Source: Computer-Aided Civil & Infrastructure Engineering. 5/9/2025, Vol. 40 Issue 12, p1606-1626. 21p.
Subjects: Deep learning, Structural panels, Image recognition (Computer vision), Feature extraction, Statistical accuracy, Inspection & review
Abstract: Accurately checking the position and presence of internal components before casting prefabricated elements is critical to ensuring product quality. However, traditional manual visual inspection is often inefficient and inaccurate. While deep learning has been widely applied to quality inspection of prefabricated components, most studies focus on surface defects and cracks, with less emphasis on the internal structural complexities of these components. Prefabricated composite panels, due to their complex structure—including small embedded parts and large‐scale reinforcing rib—require high‐precision, multiscale feature recognition. This study developed an instance segmentation model: a graph attention reasoning model (GARM) for prefabricated component detection, for the quality inspection of prefabricated concrete composite panels. First, a dataset of prefabricated concrete composite components was constructed to address the shortage of existing data and provide sufficient samples for training the segmentation network. Subsequently, after training on a self‐built dataset of prefabricated concrete composite panels, ablation experiments and comparative tests were conducted. The GARM segmentation model demonstrated superior performance in terms of detection speed and model lightweighting. Its accuracy surpassed other models, with a mean average precision (mAP50) of 88.7%. This study confirms the efficacy and reliability of the GARM instance segmentation model in detecting prefabricated concrete composite panels. [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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  Data: A graph attention reasoning model for prefabricated component detection.
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  Data: <searchLink fieldCode="JN" term="%22Computer-Aided+Civil+%26+Infrastructure+Engineering%22">Computer-Aided Civil & Infrastructure Engineering</searchLink>. 5/9/2025, Vol. 40 Issue 12, p1606-1626. 21p.
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  Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+panels%22">Structural panels</searchLink><br /><searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+accuracy%22">Statistical accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Inspection+%26+review%22">Inspection & review</searchLink>
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  Data: Accurately checking the position and presence of internal components before casting prefabricated elements is critical to ensuring product quality. However, traditional manual visual inspection is often inefficient and inaccurate. While deep learning has been widely applied to quality inspection of prefabricated components, most studies focus on surface defects and cracks, with less emphasis on the internal structural complexities of these components. Prefabricated composite panels, due to their complex structure—including small embedded parts and large‐scale reinforcing rib—require high‐precision, multiscale feature recognition. This study developed an instance segmentation model: a graph attention reasoning model (GARM) for prefabricated component detection, for the quality inspection of prefabricated concrete composite panels. First, a dataset of prefabricated concrete composite components was constructed to address the shortage of existing data and provide sufficient samples for training the segmentation network. Subsequently, after training on a self‐built dataset of prefabricated concrete composite panels, ablation experiments and comparative tests were conducted. The GARM segmentation model demonstrated superior performance in terms of detection speed and model lightweighting. Its accuracy surpassed other models, with a mean average precision (mAP50) of 88.7%. This study confirms the efficacy and reliability of the GARM instance segmentation model in detecting prefabricated concrete composite panels. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  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.13373
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        Text: English
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        Type: general
      – SubjectFull: Structural panels
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      – SubjectFull: Image recognition (Computer vision)
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      – SubjectFull: Feature extraction
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      – SubjectFull: Statistical accuracy
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      – SubjectFull: Inspection & review
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      – TitleFull: A graph attention reasoning model for prefabricated component detection.
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            NameFull: Zhou, Manxu
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            NameFull: Ye, Guanting
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            – D: 09
              M: 05
              Text: 5/9/2025
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              Y: 2025
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