LWCNet: A Lightweight and Efficient Algorithm for Household Waste Detection and Classification Based on Deep Learning

LWCNet: A Lightweight and Efficient Algorithm for Household Waste Detection and Classification Based on Deep Learning | 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 LWCNet: A Lightweight and Efficient Algorithm for Household Waste Detection and Classification Based on Deep Learning Yue Wang, Jingzhe Wang, Aixi Sun, Yu Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4629974/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract The existing waste classification algorithms based on computer vision are hindered by issues such as low classification accuracy, a large number of model parameters, and high computational complexity. Aiming at these problems, a Lightweight Waste Classification Network (LWCNet) based on the basic framework of YOLOv8n is designed and proposed in this paper. Firstly, a detection head based on the self-attention mechanism, SAHead, is proposed to capture a wider range of contextual information. Secondly, the lightweight convolution GSConv is employed to optimize the standard convolution in YOLOv8n. Combined with GELAN, the GRCSPELAN module is designed to reduce the model size without compromising performance. Finally, the AIFI module is introduced to enhance the intra-scale feature interaction ability. The experimental results indicate that LWCNet effectively reduces the model size and enhances detection efficiency, providing reliable theoretical support for the automation of household waste detection and classification. Waste classification and recycling Lightweight network Attention mechanism Computer vision YOLOv8 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 25 Jun, 2024 Submission checks completed at journal 25 Jun, 2024 First submitted to journal 24 Jun, 2024 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-4629974","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322279800,"identity":"9f427202-55d0-4867-b99a-7f42c5c5ca28","order_by":0,"name":"Yue Wang","email":"","orcid":"","institution":"Zhejiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Wang","suffix":""},{"id":322279804,"identity":"a395884e-37da-4abf-aad6-121eb63f5c21","order_by":1,"name":"Jingzhe Wang","email":"","orcid":"","institution":"Zhejiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jingzhe","middleName":"","lastName":"Wang","suffix":""},{"id":322279807,"identity":"1d82bc4c-6885-4abb-b1a8-c03e7aa39550","order_by":2,"name":"Aixi Sun","email":"","orcid":"","institution":"Zhejiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Aixi","middleName":"","lastName":"Sun","suffix":""},{"id":322279813,"identity":"a72e8fd2-8fca-476d-9fbd-27c787bf8824","order_by":3,"name":"Yu Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYFCCAyDChoGxgUQtaSRpAYPDJKiVbzx7TPJn23l75tk9Bgw/dxChhbHhXJqEZNttZsY5ZwwYe88QoYWZ4YyZhGHbbTbGGTkGzIxtRGhhA2lJbDvHQ7wWHpCWg20HJIjXIsFwxtiy4VyyAeOMtIKDvcRokZ9xxvDmjzI7e8MZyRsf/CRGC4PEAWCwsTEwGDZAY5Uw4AcqZfgDtI445aNgFIyCUTASAQCYGTND3QMiaAAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang Normal University","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-06-24 11:54:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4629974/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4629974/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60917462,"identity":"59803fa7-9456-4d1e-b177-c7a5c482a5fa","added_by":"auto","created_at":"2024-07-23 14:01:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4995696,"visible":true,"origin":"","legend":"","description":"","filename":"LWCNetALightweightandEfficientAlgorithmforHouseholdWasteDetectionandClassificationBasedonDeepLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4629974/v1_covered_00babea1-c13b-4b8c-94c7-54549ab68d85.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"LWCNet: A Lightweight and Efficient Algorithm for Household Waste Detection and Classification Based on Deep Learning","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":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Waste classification and recycling, Lightweight network, Attention mechanism, Computer vision, YOLOv8","lastPublishedDoi":"10.21203/rs.3.rs-4629974/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4629974/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The existing waste classification algorithms based on computer vision are hindered by issues such as low classification accuracy, a large number of model parameters, and high computational complexity. 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