Structure of CRU.
<div><p>Insulator defect detection is a critical task in distribution network inspections. To address issues such as low detection accuracy, high model complexity, and large parameter counts caused by the variety of insulator defect types, this study propose a lightweight multi-defect de...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
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| _version_ | 1852022541122535424 |
|---|---|
| author | Weiyu Han (3448742) |
| author2 | Zixuan Cai (20765907) Xin Li (51274) Anan Ding (20765910) Yuelin Zou (20765913) Tianjun Wang (1524403) |
| author2_role | author author author author author |
| author_facet | Weiyu Han (3448742) Zixuan Cai (20765907) Xin Li (51274) Anan Ding (20765910) Yuelin Zou (20765913) Tianjun Wang (1524403) |
| author_role | author |
| dc.creator.none.fl_str_mv | Weiyu Han (3448742) Zixuan Cai (20765907) Xin Li (51274) Anan Ding (20765910) Yuelin Zou (20765913) Tianjun Wang (1524403) |
| dc.date.none.fl_str_mv | 2025-02-21T18:32:28Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0314225.g007 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Structure_of_CRU_/28460469 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Genetics Molecular Biology Sociology Cancer Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified suppress irrelevant features siou loss function simam attention mechanism introducing scconv module improve c2f module higher average accuracy experimental results show encompasses four types target detection dataset low detection accuracy improve detection accuracy distribution network inspections accelerate model convergence insulator dataset compared high model complexity defect detection network insulator defect types defect detection network improves computational complexity shedding insulator model parameters broken insulator absent insulator study propose study creates reduces spatial lightweight algorithm effective solution critical task channel redundancy address issues |
| dc.title.none.fl_str_mv | Structure of CRU. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Insulator defect detection is a critical task in distribution network inspections. To address issues such as low detection accuracy, high model complexity, and large parameter counts caused by the variety of insulator defect types, this study propose a lightweight multi-defect detection network, LMD-YOLO, based on YOLOv8. The network improves the backbone by introducing SCConv module to improve C2f module, which reduces spatial and channel redundancy, lowering both computational complexity and the number of parameters. The SimAM attention mechanism is integrated to suppress irrelevant features and enhance feature extraction capabilities without adding extra parameters. The SIoU loss function is used in place of CIoU to accelerate model convergence and improve detection accuracy. Additionally, this study creates a target detection dataset that encompasses four types of insulators: insulator, absent insulator, broken insulator, and shedding insulator. Experimental results show that LMD-YOLO achieves a 2% higher average accuracy on the insulator dataset compared to YOLOv8n, with a 24.6% reduction in model parameters, offering an effective solution for smart grid inspections.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_477c4b2e1d6c8c4e97dcacd03d45cdf0 |
| identifier_str_mv | 10.1371/journal.pone.0314225.g007 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28460469 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Structure of CRU.Weiyu Han (3448742)Zixuan Cai (20765907)Xin Li (51274)Anan Ding (20765910)Yuelin Zou (20765913)Tianjun Wang (1524403)MedicineGeneticsMolecular BiologySociologyCancerSpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsuppress irrelevant featuressiou loss functionsimam attention mechanismintroducing scconv moduleimprove c2f modulehigher average accuracyexperimental results showencompasses four typestarget detection datasetlow detection accuracyimprove detection accuracydistribution network inspectionsaccelerate model convergenceinsulator dataset comparedhigh model complexitydefect detection networkinsulator defect typesdefect detectionnetwork improvescomputational complexityshedding insulatormodel parametersbroken insulatorabsent insulatorstudy proposestudy createsreduces spatiallightweight algorithmeffective solutioncritical taskchannel redundancyaddress issues<div><p>Insulator defect detection is a critical task in distribution network inspections. To address issues such as low detection accuracy, high model complexity, and large parameter counts caused by the variety of insulator defect types, this study propose a lightweight multi-defect detection network, LMD-YOLO, based on YOLOv8. The network improves the backbone by introducing SCConv module to improve C2f module, which reduces spatial and channel redundancy, lowering both computational complexity and the number of parameters. The SimAM attention mechanism is integrated to suppress irrelevant features and enhance feature extraction capabilities without adding extra parameters. The SIoU loss function is used in place of CIoU to accelerate model convergence and improve detection accuracy. Additionally, this study creates a target detection dataset that encompasses four types of insulators: insulator, absent insulator, broken insulator, and shedding insulator. Experimental results show that LMD-YOLO achieves a 2% higher average accuracy on the insulator dataset compared to YOLOv8n, with a 24.6% reduction in model parameters, offering an effective solution for smart grid inspections.</p></div>2025-02-21T18:32:28ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0314225.g007https://figshare.com/articles/figure/Structure_of_CRU_/28460469CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/284604692025-02-21T18:32:28Z |
| spellingShingle | Structure of CRU. Weiyu Han (3448742) Medicine Genetics Molecular Biology Sociology Cancer Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified suppress irrelevant features siou loss function simam attention mechanism introducing scconv module improve c2f module higher average accuracy experimental results show encompasses four types target detection dataset low detection accuracy improve detection accuracy distribution network inspections accelerate model convergence insulator dataset compared high model complexity defect detection network insulator defect types defect detection network improves computational complexity shedding insulator model parameters broken insulator absent insulator study propose study creates reduces spatial lightweight algorithm effective solution critical task channel redundancy address issues |
| status_str | publishedVersion |
| title | Structure of CRU. |
| title_full | Structure of CRU. |
| title_fullStr | Structure of CRU. |
| title_full_unstemmed | Structure of CRU. |
| title_short | Structure of CRU. |
| title_sort | Structure of CRU. |
| topic | Medicine Genetics Molecular Biology Sociology Cancer Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified suppress irrelevant features siou loss function simam attention mechanism introducing scconv module improve c2f module higher average accuracy experimental results show encompasses four types target detection dataset low detection accuracy improve detection accuracy distribution network inspections accelerate model convergence insulator dataset compared high model complexity defect detection network insulator defect types defect detection network improves computational complexity shedding insulator model parameters broken insulator absent insulator study propose study creates reduces spatial lightweight algorithm effective solution critical task channel redundancy address issues |