MRS-YOLO: A High-Precision Model for Efficient Waste Detection and Classification 

MRS-YOLO: A High-Precision Model for Efficient Waste Detection and Classification | 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 MRS-YOLO: A High-Precision Model for Efficient Waste Detection and Classification Yuanming Ren, Yizhe Li, XinYa Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4485704/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 With the advancement in living standards, there has been a significant surge in the quantity and diversity of household waste. To safeguard the environment and optimize resource utilization, there is an urgent demand for effective and cost-efficient intelligent waste classification methodologies. This study presents MRS-YOLO (Multi-Resolution Strategy-YOLO), a waste detection and classification model. The paper introduces the SlideLoss_IOU technique for detecting small objects, integrates RepVit into the Transformer mechanism, and devises a novel feature extraction strategy by amalgamating multi-dimensional and dynamic convolution mechanisms. These enhancements not only elevate the detection accuracy and speed but also bolster the robustness of the current YOLO model. Validation conducted on a dataset comprising 18,219 samples across 10 categories, including recyclable metal and paper, reveals a 3.53% enhancement in mAP50% accuracy compared to YOLO v8, coupled with a 15.09% reduction in volume. Furthermore, the model demonstrates improved accuracy in detecting small targets and exhibits comprehensive detection capabilities across diverse scenarios. yolo dynamic convolution waste detection Small target detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 27 May, 2024 Submission checks completed at journal 27 May, 2024 First submitted to journal 27 May, 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. 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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-4485704","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307478339,"identity":"6b4acf5b-4cf1-46c2-b102-443116c18a6d","order_by":0,"name":"Yuanming Ren","email":"","orcid":"","institution":"Qingdao University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuanming","middleName":"","lastName":"Ren","suffix":""},{"id":307478341,"identity":"08b59a78-86a3-4067-9c1d-2e66d2cdbc08","order_by":1,"name":"Yizhe Li","email":"","orcid":"","institution":"Qingdao University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yizhe","middleName":"","lastName":"Li","suffix":""},{"id":307478343,"identity":"8d4c4f71-6cb4-436a-8ec4-215a7006b62b","order_by":2,"name":"XinYa Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYBACfmbGhgMfDCTk2JiZj3/4ABE0wKtFsr258eGMCgtjfva2NMYZDAwSBLUYnDnebMxzpiJxZs8ZNWYeorTcSGyT4G2TMDa4kcP22KbscB0De/M2CYaaO7gdBtIi2SYhZ3Aj97hxzrnDEgw8x8okGI49w6mFD6TFEGxLXoJ0bhtQi0SOmQRjw2HcLgNpAaENN3IMpC1BWuTf4NcicOZgs8GBMxIg75tJM4Jt4cGvRbK9sfFhQ4UEKJCTDXvOpUu28aQVWyQcw62Fn5n9weE/BnWgqDz44EeZNT8/++GNNz7U4PELKmADIwaGBGI1QNWPglEwCkbBKEAFAAe5WKJpAdIHAAAAAElFTkSuQmCC","orcid":"","institution":"Qingdao University of Technology","correspondingAuthor":true,"prefix":"","firstName":"XinYa","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2024-05-27 14:25:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4485704/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4485704/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58039925,"identity":"8e34c59d-d1af-4c68-82a7-15a4299fa432","added_by":"auto","created_at":"2024-06-10 10:02:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":593602,"visible":true,"origin":"","legend":"","description":"","filename":"MRSYOLO2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4485704/v1_covered_044f5354-7186-451c-ab32-7243455df09c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"MRS-YOLO: A High-Precision Model for Efficient Waste Detection and Classification ","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-visual-computer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tvcj","sideBox":"Learn more about [The Visual Computer](http://link.springer.com/journal/371)","snPcode":"371","submissionUrl":"https://submission.nature.com/new-submission/371/3","title":"The Visual Computer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"yolo, dynamic convolution, waste detection, Small target detection","lastPublishedDoi":"10.21203/rs.3.rs-4485704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4485704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the advancement in living standards, there has been a significant surge in the quantity and diversity of household waste. 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