A New Lightweight Network for Real-time Detection of Railway Infrastructure

A New Lightweight Network for Real-time Detection of Railway Infrastructure | 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 A New Lightweight Network for Real-time Detection of Railway Infrastructure Yu Cheng, ruijun Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3932509/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 deployment of deep convolutional neural network in railway infrastructure real-time detection is largely hindered by huge computing costs. This paper proposes a new lightweight network called S-CA-YOLOv5, in which a new backbone combined ShuffleNet with Coordinate Attention is used to reduce the computational complexity, and the CA mechanism can capture regions of interest accurately. Moreover, the improved loss function with Efficient Intersection over Union facilitates more accurate localization and classification, which is conducive to higher detection precision and faster convergence speed. Finally, through channel pruning combined with knowledge distillation fine-tuning, the model is further lightweight. Compared with other methods, our proposed method achieves the best trade-off between detection precision and speed. Hence, the proposed method has the potential to achieve real-time detection for the railway infrastructure based on embedded Graphic Processing Unit platforms. Railway infrastructure detection YOLOv5 ShuffleNet Coordinate Attention 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-3932509","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271560960,"identity":"c1b8b31e-bd12-4bac-9dad-1476547206c1","order_by":0,"name":"Yu Cheng","email":"","orcid":"","institution":"Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Cheng","suffix":""},{"id":271560961,"identity":"ffce86ef-d925-4a06-a403-a7e8ef594547","order_by":1,"name":"ruijun Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYNACHhsefvYG0rSkyUj2HCDNmsM2BjcciFQrPyP34OcKmfM8DDcYGD98zCFCi8GNvGTJMzy3eRhnNzBLztxGjBaJHDPGBqAWZpkDbMy8xGiRnwHWco6HTSKBSC0MN8BaDvDwEK3F4MwbY8kGnmQeCZ6DzcT5Rb49x/BjY4+dvf3x5oMfPhLlMBBg7AGTDcSqB4EfpCgeBaNgFIyCEQcA3PUvpMzh5vIAAAAASUVORK5CYII=","orcid":"","institution":"School of Electrical and Control Engineering, North University of China","correspondingAuthor":true,"prefix":"","firstName":"ruijun","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2024-02-06 02:44:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3932509/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3932509/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50940856,"identity":"e3de7739-058a-420c-8adf-0ee794938fc6","added_by":"auto","created_at":"2024-02-10 01:07:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4262626,"visible":true,"origin":"","legend":"","description":"","filename":"LightweightNetwork.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3932509/v1_covered_0c398252-14cf-4ccb-875b-0cd69dc9df82.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A New Lightweight Network for Real-time Detection of Railway Infrastructure","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":"Railway infrastructure detection, YOLOv5, ShuffleNet, Coordinate Attention","lastPublishedDoi":"10.21203/rs.3.rs-3932509/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3932509/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The deployment of deep convolutional neural network in railway infrastructure real-time detection is largely hindered by huge computing costs. 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