Lite-FARNet: A Light-weight Feedback Attention Residual Network for Efficient Multi-Class Segmentation in Complex Urban Scenes

Lite-FARNet: A Light-weight Feedback Attention Residual Network for Efficient Multi-Class Segmentation in Complex Urban Scenes | 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 Article Lite-FARNet: A Light-weight Feedback Attention Residual Network for Efficient Multi-Class Segmentation in Complex Urban Scenes Jiaxi Yang, Jiaquan Shen, Shitong Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7339928/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 Image segmentation is one of the most important techniques in computer vision, forming the foundation for biomedical imaging and serving as a key component in urban planning and autonomous driving. In this project, we present a novel lightweight feedback attention residual network (Lite-FARNet) specifically designed to address the complex demands of multi-class segmentation in urban scenes, evaluated using the Cityscapes dataset. To effectively manage multi-class predictions, we introduce a competitive inference layer, while spatial and channel squeeze-and-excitation residual module are incorporated to enhance feature representation and improve context awareness. The streamlined architecture design significantly reduces computational complexity and memory usage, making it advantageous for deployment in resource-constrained environments. Extensive testing and evaluation demonstrate that the proposed network consistently delivers robust adaptability and strong accuracy across diverse and challenging urban environments. These findings highlight the model’s versatility, efficiency, and potential as a powerful tool for a wide range of image segmentation tasks, as well as its suitability for deployment in resource-constrained devices and real-time intelligent systems. Physical sciences/Engineering Physical sciences/Mathematics and computing 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. 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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-7339928","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":563687482,"identity":"754e4949-8efd-4aa3-acd3-4d9d36f75e5e","order_by":0,"name":"Jiaxi Yang","email":"","orcid":"","institution":"Concordia University","correspondingAuthor":false,"prefix":"","firstName":"Jiaxi","middleName":"","lastName":"Yang","suffix":""},{"id":563687486,"identity":"cf673215-192e-48df-b9f5-cbd209c4e39b","order_by":1,"name":"Jiaquan Shen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDACCRA2gHNtePj5G0jTkiYjOeMAEVqQwGEbg4YE/DrkZzcfe2BRYCe74fjZAww/287zGDAcYPzwMQe3FoM7x9INJAySjTecyUtg7G27zWPO3MAsOXMbHi0SOWYSEgbMiRsO5BgwM5y5zWPZcICNmRePFvkZ+d+AWuoTN5x/A9JyjsfgQAJ+LQw3ctiAWg4nbrgBsqXiAGEtBjfSQA47bjzzxhsDxp6KZB7JGQeb8fpFfkbyM2mJP9WyfedzDBh+GNjZ8/M3H/zwEZ/DgIAZGDeMDQwM7D8gfBCbAGD8QJSyUTAKRsEoGLEAAFbCTwI/nmc3AAAAAElFTkSuQmCC","orcid":"","institution":"Luoyang Normal University","correspondingAuthor":true,"prefix":"","firstName":"Jiaquan","middleName":"","lastName":"Shen","suffix":""},{"id":563687487,"identity":"9a8772ff-eba5-4d91-b492-6eec1389abb6","order_by":2,"name":"Shitong Wang","email":"","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Shitong","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-08-10 16:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7339928/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7339928/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98843806,"identity":"dac5841f-ae6c-4869-8072-aab342aecd98","added_by":"auto","created_at":"2025-12-23 04:04:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8918353,"visible":true,"origin":"","legend":"","description":"","filename":"LiteFARNetLightweightFeedbackAttentionNetworksforMultiClassSegmentationinUrbanScenes.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7339928/v1/60aa32f1c9baa2939b704f73.pdf"},{"id":98843805,"identity":"2c27d790-c75e-40ea-b8a8-bb11df30b915","added_by":"auto","created_at":"2025-12-23 04:04:27","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5291,"visible":true,"origin":"","legend":"","description":"","filename":"0db276525aa9407e9c0d326af97590b6.json","url":"https://assets-eu.researchsquare.com/files/rs-7339928/v1/1edfedf97e4cf5b3288d8e66.json"},{"id":101858686,"identity":"3031bfb8-08bf-47d3-bbb0-d958cc9d4faf","added_by":"auto","created_at":"2026-02-04 11:13:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3028498,"visible":true,"origin":"","legend":"","description":"","filename":"LiteFARNetLightweightFeedbackAttentionNetworksforMultiClassSegmentationinUrbanScenes.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7339928/v1_covered_ffebde31-5abc-4d4b-9b92-7c54435db030.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lite-FARNet: A Light-weight Feedback Attention Residual Network for Efficient Multi-Class Segmentation in Complex Urban Scenes","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":"","lastPublishedDoi":"10.21203/rs.3.rs-7339928/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7339928/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Image segmentation is one of the most important techniques in computer vision, forming the foundation for biomedical imaging and serving as a key component in urban planning and autonomous driving. 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