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Lightweight network for insulator fault detection based on improved YOLOv5.

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Title: Lightweight network for insulator fault detection based on improved YOLOv5.
Authors: Weng, Dehua (AUTHOR), Zhu, Zhiliang (AUTHOR), Yan, Zhengbing (AUTHOR), Wu, Moran (AUTHOR), Jiang, Ziang (AUTHOR), Ye, Nan (AUTHOR)
Source: Connection Science. Dec2024, Vol. 36 Issue 1, p1-19. 19p.
Subjects: Fault diagnosis, Fault currents, Inspection & review, Deep learning, Flashover
Abstract: Severe damage to insulators can hinder the daily operation of the power system. Current fault diagnosis methods heavily depend on manual visual inspection, leading to inefficiency and inaccuracies. While computer vision algorithms have been developed, their high requirements for running environments limit their applicability on edge devices. Additionally, the challenges in identifying insulator flashover faults have resulted in limited effectiveness in fault diagnosis. To address these issues, we introduce a novel one-stage network that enables real-time detection of insulator faults on mobile devices. We designed a new module that optimises the computational complexity of networks and fused the module with the attention mechanism SimAM to solve the problem of low efficiency in detecting flashover faults. Our research deploys multiple models on embedded devices in this article. Results indicate that the YOLOv5s-L-SimAM achieves the mAP of 93.9% and the model size is compressed to 9.4 MB, achieving the frame rate of 9.5 in the Jetson Xavier NX. [ABSTRACT FROM AUTHOR]
Copyright of Connection Science is the property of Taylor & Francis Ltd 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: Psychology and Behavioral Sciences Collection
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  Data: Lightweight network for insulator fault detection based on improved YOLOv5.
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  Data: <searchLink fieldCode="AR" term="%22Weng%2C+Dehua%22">Weng, Dehua</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Zhiliang%22">Zhu, Zhiliang</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yan%2C+Zhengbing%22">Yan, Zhengbing</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Moran%22">Wu, Moran</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Ziang%22">Jiang, Ziang</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ye%2C+Nan%22">Ye, Nan</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Connection+Science%22">Connection Science</searchLink>. Dec2024, Vol. 36 Issue 1, p1-19. 19p.
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  Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+currents%22">Fault currents</searchLink><br /><searchLink fieldCode="DE" term="%22Inspection+%26+review%22">Inspection & review</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Flashover%22">Flashover</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Severe damage to insulators can hinder the daily operation of the power system. Current fault diagnosis methods heavily depend on manual visual inspection, leading to inefficiency and inaccuracies. While computer vision algorithms have been developed, their high requirements for running environments limit their applicability on edge devices. Additionally, the challenges in identifying insulator flashover faults have resulted in limited effectiveness in fault diagnosis. To address these issues, we introduce a novel one-stage network that enables real-time detection of insulator faults on mobile devices. We designed a new module that optimises the computational complexity of networks and fused the module with the attention mechanism SimAM to solve the problem of low efficiency in detecting flashover faults. Our research deploys multiple models on embedded devices in this article. Results indicate that the YOLOv5s-L-SimAM achieves the mAP of 93.9% and the model size is compressed to 9.4 MB, achieving the frame rate of 9.5 in the Jetson Xavier NX. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Connection Science is the property of Taylor & Francis Ltd 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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      – Type: doi
        Value: 10.1080/09540091.2023.2284090
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      – Code: eng
        Text: English
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        PageCount: 19
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        Type: general
      – SubjectFull: Fault currents
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      – SubjectFull: Inspection & review
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      – SubjectFull: Deep learning
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      – SubjectFull: Flashover
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              M: 12
              Text: Dec2024
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