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Improved Binocular Localization of Bolts in Substation Based on Clamp and Bolts Detection Using YOLOv11 for Robotic Maintaining.

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Title: Improved Binocular Localization of Bolts in Substation Based on Clamp and Bolts Detection Using YOLOv11 for Robotic Maintaining.
Authors: Wu, Xuxiang1 2505889912@qq.com, Wu, Xiaoliang1 996591156@qq.com, Zhou, Qihui1 2751921426@qq.com, Huang, Haoming1 2724756219@qq.com, Gao, Changqing2 gcq@hnu.edu.cn, Fang, Qiu3 qfang@hnu.edu.cn, Zhang, Xiaogang4 zhangxg@hnu.edu.cn
Source: Engineering Letters. Jul2026, Vol. 34 Issue 7, p2488-2497. 10p.
Subjects: Binocular vision, Object recognition (Computer vision), Triangulation, Stereo image processing, Clamps (Engineering), Electric substations, Robotics
Abstract: Accurate bolt localization is a prerequisite for robotic maintenance in substations. However, conventional binocular vision methods often rely on global stereo matching and are therefore vulnerable to mismatches in complex outdoor environments with similar textures, and specular reflections. To address this issue, this paper proposes an improved binocular localization framework for substation bolts. First, YOLOv11 is used to detect bolts in stereo images and clamps in the left image. Then, a clamp-bolt pairing strategy and a clamp-guided stereo matching scheme are introduced to establish robust bolt correspondences while constraining the matching search space. Finally, the coordinates of bolts are recovered from the matched feature points using binocular triangulation. Experiments conducted in real substation scenarios demonstrate that the proposed method achieves millimeter-level localization accuracy, reducing localization errors by 47.1% and 43.7% compared with clamp localization and depth-image-based localization, respectively. The proposed framework effectively alleviates mismatch-induced localization errors and provides a reliable spatial perception solution for robotic bolt-maintenance tasks in substations. [ABSTRACT FROM AUTHOR]
Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) 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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DbLabel: Engineering Source
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PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: Improved Binocular Localization of Bolts in Substation Based on Clamp and Bolts Detection Using YOLOv11 for Robotic Maintaining.
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  Data: <searchLink fieldCode="AR" term="%22Wu%2C+Xuxiang%22">Wu, Xuxiang</searchLink><relatesTo>1</relatesTo><i> 2505889912@qq.com</i><br /><searchLink fieldCode="AR" term="%22Wu%2C+Xiaoliang%22">Wu, Xiaoliang</searchLink><relatesTo>1</relatesTo><i> 996591156@qq.com</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Qihui%22">Zhou, Qihui</searchLink><relatesTo>1</relatesTo><i> 2751921426@qq.com</i><br /><searchLink fieldCode="AR" term="%22Huang%2C+Haoming%22">Huang, Haoming</searchLink><relatesTo>1</relatesTo><i> 2724756219@qq.com</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Changqing%22">Gao, Changqing</searchLink><relatesTo>2</relatesTo><i> gcq@hnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Fang%2C+Qiu%22">Fang, Qiu</searchLink><relatesTo>3</relatesTo><i> qfang@hnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xiaogang%22">Zhang, Xiaogang</searchLink><relatesTo>4</relatesTo><i> zhangxg@hnu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. Jul2026, Vol. 34 Issue 7, p2488-2497. 10p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Binocular+vision%22">Binocular vision</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Triangulation%22">Triangulation</searchLink><br /><searchLink fieldCode="DE" term="%22Stereo+image+processing%22">Stereo image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Clamps+%28Engineering%29%22">Clamps (Engineering)</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+substations%22">Electric substations</searchLink><br /><searchLink fieldCode="DE" term="%22Robotics%22">Robotics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Accurate bolt localization is a prerequisite for robotic maintenance in substations. However, conventional binocular vision methods often rely on global stereo matching and are therefore vulnerable to mismatches in complex outdoor environments with similar textures, and specular reflections. To address this issue, this paper proposes an improved binocular localization framework for substation bolts. First, YOLOv11 is used to detect bolts in stereo images and clamps in the left image. Then, a clamp-bolt pairing strategy and a clamp-guided stereo matching scheme are introduced to establish robust bolt correspondences while constraining the matching search space. Finally, the coordinates of bolts are recovered from the matched feature points using binocular triangulation. Experiments conducted in real substation scenarios demonstrate that the proposed method achieves millimeter-level localization accuracy, reducing localization errors by 47.1% and 43.7% compared with clamp localization and depth-image-based localization, respectively. The proposed framework effectively alleviates mismatch-induced localization errors and provides a reliable spatial perception solution for robotic bolt-maintenance tasks in substations. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) 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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    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: 2488
    Subjects:
      – SubjectFull: Binocular vision
        Type: general
      – SubjectFull: Object recognition (Computer vision)
        Type: general
      – SubjectFull: Triangulation
        Type: general
      – SubjectFull: Stereo image processing
        Type: general
      – SubjectFull: Clamps (Engineering)
        Type: general
      – SubjectFull: Electric substations
        Type: general
      – SubjectFull: Robotics
        Type: general
    Titles:
      – TitleFull: Improved Binocular Localization of Bolts in Substation Based on Clamp and Bolts Detection Using YOLOv11 for Robotic Maintaining.
        Type: main
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          Name:
            NameFull: Wu, Xuxiang
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            NameFull: Wu, Xiaoliang
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            NameFull: Zhou, Qihui
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            NameFull: Huang, Haoming
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            NameFull: Gao, Changqing
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            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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