EffDetNet：Abnormal Detection of Dampers Based on Lightweight Network with Shared Weights of Fused Multidimensional Features

EffDetNet：Abnormal Detection of Dampers Based on Lightweight Network with Shared Weights of Fused Multidimensional Features | 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 EffDetNet：Abnormal Detection of Dampers Based on Lightweight Network with Shared Weights of Fused Multidimensional Features Chao Ji, Zhenyu Li, Junpeng Liu, Siyuan Zhou, Jiayi He, Jingyu Mu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5911642/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The vibration dampers on transmission lines, after prolonged exposure to natural environments, are prone to corrosion and damage caused by various external factors. Detection algorithms specifically designed for small targets like vibration dampers are still scarce. Moreover, existing algorithms often exhibit drawbacks such as limited accuracy and insufficient real-time performance.To address these challenges, this paper proposes a novel detection method for abnormal vibration dampers on transmission lines based on an EffDetNet framework. Specifically, Multi-dimensional Separable Concatenated Residual Convolution module (MDSCR) is introduced to enhance contextual connections through multi-scale feature extraction. By integrating feature fusion, the model preserves more detailed features, while depthwise separable convolution reduces computational overhead, improving both accuracy and detection speed. Additionally, a Dynamic Allocation Attention mechanism (DAAM) is proposed to effectively capture diverse dimensional information, thereby enhancing feature focus and perceptual capabilities.To further optimize performance, we design a lightweight re-parameterized convolution and shared convolution detection head (LRSC), which significantly reduces model parameters and enhances computational efficiency. Finally, we adopt a Shape-IoU bounding box regression loss function to achieve faster convergence and higher detection precision.Experimental results validate the high efficiency and performance of the EffDetNet model. On the dataset constructed for this study, the model achieves a mean Average Precision (mAP) of 94.3% and a Frames Per Second (FPS) rate of 128.3. Leveraging this model, we developed an abnormal vibration damper monitoring system for transmission lines, demonstrating significant practical engineering value. Transmission Line Abnormal Vibration Damper Detection Dynamic Allocation Attention Mechanism Model Lightweighting Shape-IoU Monitoring System Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5911642","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":408428392,"identity":"7f1b9e8a-e4eb-407f-9bce-4627f988f2eb","order_by":0,"name":"Chao Ji","email":"","orcid":"","institution":"Xi’an Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Ji","suffix":""},{"id":408428393,"identity":"9317c868-f8f4-4d36-b7b2-3dde02358dc8","order_by":1,"name":"Zhenyu Li","email":"","orcid":"","institution":"Xi’an Polytechnic 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Mechanism, Model Lightweighting, Shape-IoU, Monitoring System","lastPublishedDoi":"10.21203/rs.3.rs-5911642/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5911642/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe vibration dampers on transmission lines, after prolonged exposure to natural environments, are prone to corrosion and damage caused by various external factors. Detection algorithms specifically designed for small targets like vibration dampers are still scarce. Moreover, existing algorithms often exhibit drawbacks such as limited accuracy and insufficient real-time performance.To address these challenges, this paper proposes a novel detection method for abnormal vibration dampers on transmission lines based on an EffDetNet framework. Specifically, Multi-dimensional Separable Concatenated Residual Convolution module (MDSCR) is introduced to enhance contextual connections through multi-scale feature extraction. By integrating feature fusion, the model preserves more detailed features, while depthwise separable convolution reduces computational overhead, improving both accuracy and detection speed. Additionally, a Dynamic Allocation Attention mechanism (DAAM) is proposed to effectively capture diverse dimensional information, thereby enhancing feature focus and perceptual capabilities.To further optimize performance, we design a lightweight re-parameterized convolution and shared convolution detection head (LRSC), which significantly reduces model parameters and enhances computational efficiency. Finally, we adopt a Shape-IoU bounding box regression loss function to achieve faster convergence and higher detection precision.Experimental results validate the high efficiency and performance of the EffDetNet model. On the dataset constructed for this study, the model achieves a mean Average Precision (mAP) of 94.3% and a Frames Per Second (FPS) rate of 128.3. Leveraging this model, we developed an abnormal vibration damper monitoring system for transmission lines, demonstrating significant practical engineering value.\u003c/p\u003e","manuscriptTitle":"EffDetNet：Abnormal Detection of Dampers Based on Lightweight Network with Shared Weights of Fused Multidimensional Features","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-30 03:20:24","doi":"10.21203/rs.3.rs-5911642/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8595b931-7902-4c29-867b-8f9911b6b938","owner":[],"postedDate":"January 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-23T14:08:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-30 03:20:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5911642","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5911642","identity":"rs-5911642","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}