Garbage Overflow Detection Algorithm Based on Improved YOLOv8n

Garbage Overflow Detection Algorithm Based on Improved YOLOv8n | 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 Garbage Overflow Detection Algorithm Based on Improved YOLOv8n Feiyang Liu, Ruiqi Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4691427/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 Unprecedented urbanization and population growth have led to an increasing amount of domestic waste, posing significant challenges to urban environments and aesthetics worldwide. This paper proposes an improved garbage detection system based on YOLOv8n. The system uses target detection algorithms to identify garbage and trash cans in road images, determining if trash cans are overflowing and need cleaning. Firstly, a Deformable Attention Mechanism (DAT) is introduced into the backbone network, incorporating dynamic sampling points. Secondly, an auxiliary head is integrated into the model's head for training, providing deep supervision. Finally, a novel Inner-MPDIoU loss function is proposed to offer more precise evaluation results and enhance generalization ability. Comparative experiments show that the algorithm achieves a mean Average Precision (mAP) of 94.0%, significantly improving feature extraction and detection accuracy. YOLO v8n DAT auxiliary Head Inner-MPDIoU 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-4691427","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":325856405,"identity":"60e20b40-df65-4782-ba27-d1b6c749c4fb","order_by":0,"name":"Feiyang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACNvbmgw8+/pGQY2M4fIA4LXw8x5INZzZYGPMxHksgToucRI6aMG9DReI85jMGRDpMIoeNmXeHRGIb25mPN94w2MnpNhDSwvP22MO5ZySM23jObracw5BsbHaAkBb2vHSDN2wSsm0SZ7dJ8zAcSNxGUAtDjpkED5sEY5v8m2dEauHIMZPkbZNQbGM4w0akFlAgzwD6hY3hmLHlHAMi/CLfDozKDxV1cvINhx/eeFNhJ0dQCwqQ4CEyapC1kKpjFIyCUTAKRgQAAAfbQkI+d5FiAAAAAElFTkSuQmCC","orcid":"","institution":"Chengdu University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Feiyang","middleName":"","lastName":"Liu","suffix":""},{"id":325856406,"identity":"cc84065b-c3ae-4484-b89f-defd6f2ca91e","order_by":1,"name":"Ruiqi Zhou","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ruiqi","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-07-05 10:03:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4691427/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4691427/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66819673,"identity":"e9675646-7b33-4e3c-87be-5fb7ca4010ed","added_by":"auto","created_at":"2024-10-16 20:46:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":650217,"visible":true,"origin":"","legend":"","description":"","filename":"GarbageOverflowDetectionAlgorithmBasedonImprovedYOLOv8n.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4691427/v1_covered_248d7974-d7ad-40bf-87f4-5c75ba54c1ae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Garbage Overflow Detection Algorithm Based on Improved YOLOv8n","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":"YOLO v8n, DAT, auxiliary Head, Inner-MPDIoU","lastPublishedDoi":"10.21203/rs.3.rs-4691427/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4691427/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnprecedented urbanization and population growth have led to an increasing amount of domestic waste, posing significant challenges to urban environments and aesthetics worldwide. This paper proposes an improved garbage detection system based on YOLOv8n. The system uses target detection algorithms to identify garbage and trash cans in road images, determining if trash cans are overflowing and need cleaning. Firstly, a Deformable Attention Mechanism (DAT) is introduced into the backbone network, incorporating dynamic sampling points. Secondly, an auxiliary head is integrated into the model's head for training, providing deep supervision. Finally, a novel Inner-MPDIoU loss function is proposed to offer more precise evaluation results and enhance generalization ability. 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