Multi-path Hybrid Attention Deep Neural Network for Valve Detection

Multi-path Hybrid Attention Deep Neural Network for Valve Detection | 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 Multi-path Hybrid Attention Deep Neural Network for Valve Detection First Xuefeng Zhang, Second Yonghe Huang, Xiwen Qu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4618247/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 Valves Detection is a basic function of rescue robots in various disaster situations. However, due to the small differences between similar valves, rescue robots suffer great challenges in valve detection in complex environments. To address this challenge, this paper proposes a multi-path hybrid attention deep neural network (MHADNN). By weighting features at different scales and spatial positions, the MHADNN can help valve detection models focus on more discriminative subtle features, thereby enhancing the ability to distinguish similar valves. This paper combines the MHADNN with the YOLOv5n to develop a valve detection model. The comparative experiments are conducted on a similar valve dataset collected in the simulated environment of a chemical industrial park. The experimental results show that compared with YOLOv5n, the proposed valve detection model has an average precision improvement of 4.20%. It has excellent performance in distinguishing similar valves. Valve detection Rescue robots Attention deep neural network YOLOv5n 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-4618247","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317634133,"identity":"72561160-871e-4abd-9b75-083ee258ecac","order_by":0,"name":"First Xuefeng Zhang","email":"","orcid":"","institution":"Anhui University of Technology","correspondingAuthor":false,"prefix":"","firstName":"First","middleName":"Xuefeng","lastName":"Zhang","suffix":""},{"id":317634134,"identity":"1d183c14-0ddd-48d4-8d82-77696d6bfca3","order_by":1,"name":"Second Yonghe Huang","email":"","orcid":"","institution":"Anhui University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Second","middleName":"Yonghe","lastName":"Huang","suffix":""},{"id":317634135,"identity":"fa5bc489-bda9-44c8-b8d4-6b03078a23fe","order_by":2,"name":"Xiwen Qu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYLACxgYGBn4GHjibOC0Skg1ALQdI0mJwgFgtfDdyDBh+7jhcZ3z87DHpDww2shsOMD97gE+L5JkzBoy9Zw5LmJ3JS5M4wJBmvOEAm7kBPi0Gx3s3MPC2AbUcyDEDajmcuOEAD5sEXi2HeTcw/gVqMe5/A9LynwgtQFuYQbYYSIBtOUBYi+SZ8x+YZdvSJWfceGNsccYg2XjmYTYzvFr4bqQlML5ts+bn788xvFFRYSfbd7z5GV4twKhg/4HkTiBmxqserGUUjIJRMApGAQEAAIsOTCVJ7NwoAAAAAElFTkSuQmCC","orcid":"","institution":"Anhui University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Xiwen","middleName":"","lastName":"Qu","suffix":""}],"badges":[],"createdAt":"2024-06-21 16:17:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4618247/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4618247/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61667123,"identity":"20490a11-01ab-4dd8-b63d-5476a41c1c26","added_by":"auto","created_at":"2024-08-02 16:31:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":817835,"visible":true,"origin":"","legend":"","description":"","filename":"MS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4618247/v1_covered_b315741c-77c6-4126-adad-7211f3cb0222.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-path Hybrid Attention Deep Neural Network for Valve Detection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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},"keywords":"Valve detection, Rescue robots, Attention deep neural network, YOLOv5n","lastPublishedDoi":"10.21203/rs.3.rs-4618247/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4618247/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Valves Detection is a basic function of rescue robots in various disaster situations. However, due to the small differences between similar valves, rescue robots suffer great challenges in valve detection in complex environments. To address this challenge, this paper proposes a multi-path hybrid attention deep neural network (MHADNN). By weighting features at different scales and spatial positions, the MHADNN can help valve detection models focus on more discriminative subtle features, thereby enhancing the ability to distinguish similar valves. This paper combines the MHADNN with the YOLOv5n to develop a valve detection model. The comparative experiments are conducted on a similar valve dataset collected in the simulated environment of a chemical industrial park. The experimental results show that compared with YOLOv5n, the proposed valve detection model has an average precision improvement of 4.20%. It has excellent performance in distinguishing similar valves.","manuscriptTitle":"Multi-path Hybrid Attention Deep Neural Network for Valve Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-12 10:36:23","doi":"10.21203/rs.3.rs-4618247/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":"d0ec5949-8d69-4bb7-8db1-786951d5aa9f","owner":[],"postedDate":"July 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-02T16:23:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-12 10:36:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4618247","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4618247","identity":"rs-4618247","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}