HEYOLO-IDD: An efficient insulator defect detection network based on the improved YOLOv11n

HEYOLO-IDD: An efficient insulator defect detection network based on the improved YOLOv11n | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article HEYOLO-IDD: An efficient insulator defect detection network based on the improved YOLOv11n Dahua Li, Jiale Zhang, Junfang Li, Dong Li, Xueying Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9258709/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The automated detection of insulator defects is a crucial component in driving the intelligent development and construction of new power systems. To address limitations such as accuracy and generalisation in insulator defect detection, this paper proposes HEYOLO-IDD, a high-efficiency detection model based on YOLOv11n. Firstly,KernelWarehouse-Conv(KWConv)is introduced as a downsampling module to reduce redundant parameters in the backbone network and lower computational complexity. Secondly, to address the characteristics of defects, a Partial Multi-Scale Conv(PMC) module is designed to replace the Bottleneck in CSP. Combined with TripletAttention, this forms the CSP-PMC-TripletAttention(CPT) module, which enhances multi-scale and small-object feature extraction capabilities without increasing the parameter burden. This enables an innovative fusion of the backbone network based on KWConv and CPT. Finally, the bi-directional feature pyramid network (BiFPN) neck network is adopted to enhance feature fusion and simplify the architecture. On our self-built HEData-2025 dataset, compared to YOLOv11n, mAP(0.5) improves by 7.2% whilst the number of parameters is reduced by 23.2%, demonstrating superior overall performance. The generalisation capability is further validated on the public VisDrone-2019 dataset. Deep learning Insulator defect detection Attention mechanism YOLO Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 05 Apr, 2026 Editor assigned by journal 30 Mar, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 29 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9258709","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619242961,"identity":"2a5187e3-4661-4ad9-8865-8e5346672a2f","order_by":0,"name":"Dahua Li","email":"","orcid":"","institution":"Tianjin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Dahua","middleName":"","lastName":"Li","suffix":""},{"id":619242964,"identity":"9af5d932-f61e-4c4c-a016-c105d6da822a","order_by":1,"name":"Jiale Zhang","email":"","orcid":"","institution":"Tianjin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiale","middleName":"","lastName":"Zhang","suffix":""},{"id":619242966,"identity":"d573b156-9bdc-45b6-b05d-ae83304c019a","order_by":2,"name":"Junfang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACxvbGxsc//9nU7z/eQKQW5p7Dh40Z2NIYG84cIFIL+wy3NGEGtsOMDTcSiNTCO4PHjLmAh5mZcebjjTcYamyiCWqRnN1j9niGBBsbs3RasQXDsbTcBkJaDOecMTfgMeDhYZPOMZNgbDhMWIv9DaBKngQJCR7JM0RqYZyRlibNc8DAAKiJWC3AQDac2ZCQYMAD9EsCMX4BReWDjw3/EwzYD2+88aHGhrAWZGAgkUCKcogWUnWMglEwCkbByAAAXuM/cj0a+mIAAAAASUVORK5CYII=","orcid":"","institution":"Tianjin University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Junfang","middleName":"","lastName":"Li","suffix":""},{"id":619242968,"identity":"05b843f4-b927-495e-be79-22ab8e8d6762","order_by":3,"name":"Dong Li","email":"","orcid":"","institution":"Tianjin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Li","suffix":""},{"id":619242969,"identity":"befe6799-073a-4af5-af05-513da46e965f","order_by":4,"name":"Xueying Hu","email":"","orcid":"","institution":"Tianjin Tianchuan Electrical Control Equipment Testing Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Xueying","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2026-03-29 12:23:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9258709/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9258709/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106491311,"identity":"3c8207e5-21d4-43b3-a043-1384420d7235","added_by":"auto","created_at":"2026-04-09 07:28:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":486305,"visible":true,"origin":"","legend":"","description":"","filename":"snarticletemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9258709/v1_covered_7f325e9c-62d7-40bf-95ec-7f59230020cd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"HEYOLO-IDD: An efficient insulator defect detection network based on the improved YOLOv11n","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Deep learning, Insulator defect detection, Attention mechanism, YOLO","lastPublishedDoi":"10.21203/rs.3.rs-9258709/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9258709/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe automated detection of insulator defects is a crucial component in driving the intelligent development and construction of new power systems. To address limitations such as accuracy and generalisation in insulator defect detection, this paper proposes HEYOLO-IDD, a high-efficiency detection model based on YOLOv11n. Firstly,KernelWarehouse-Conv(KWConv)is introduced as a downsampling module to reduce redundant parameters in the backbone network and lower computational complexity. Secondly, to address the characteristics of defects, a Partial Multi-Scale Conv(PMC) module is designed to replace the Bottleneck in CSP. Combined with TripletAttention, this forms the CSP-PMC-TripletAttention(CPT) module, which enhances multi-scale and small-object feature extraction capabilities without increasing the parameter burden. This enables an innovative fusion of the backbone network based on KWConv and CPT. Finally, the bi-directional feature pyramid network (BiFPN) neck network is adopted to enhance feature fusion and simplify the architecture. On our self-built HEData-2025 dataset, compared to YOLOv11n, mAP(0.5) improves by 7.2% whilst the number of parameters is reduced by 23.2%, demonstrating superior overall performance. 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