MPE-YOLO: Enhanced Small Target Detection in Aerial Imaging

MPE-YOLO: Enhanced Small Target Detection in Aerial Imaging | 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 MPE-YOLO: Enhanced Small Target Detection in Aerial Imaging Jia Su, Yichang Qin, Ze Jia, Ben Liang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3998400/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Aerial image target detection is crucial for urban planning, traffic monitoring, and disaster assessment, but existing detection algorithms face challenges in small target recognition and accuracy within complex environments. To address this issue, this paper proposes an improved model based on YOLOv8, named MPE-YOLO. Initially, a multilevel feature integrator (MFI) module is employed to enhance the representation of small target features, which meticulously moderates information loss during the feature fusion process. For the backbone network of the model, a perception enhancement convolution (PEC) module is introduced to replace traditional convolutional layers, thereby expanding the network’s fine-grained feature processing capability. Furthermore, an enhanced scope-C2f (ES-C2f) module is designed, which utilizes channel expansion and stacking of multiscale convolutional kernels to enhance the network’s ability to capture small target details. After a series of experiments on the VisDrone, RSOD, and AI-TOD datasets, the model has not only demonstrated superior performance in aerial image detection tasks compared to existing advanced algorithms but also achieved a lightweight model structure. The experimental results prove the potential of MPE-YOLO in enhancing the accuracy and operational efficiency of aerial target detection. Physical sciences/Engineering/Aerospace engineering Physical sciences/Engineering/Electrical and electronic engineering Object detection Aerial image Small target Model lightweight YOLOv8 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 25 May, 2024 Reviews received at journal 25 May, 2024 Reviewers agreed at journal 15 May, 2024 Reviews received at journal 15 Apr, 2024 Reviewers agreed at journal 03 Apr, 2024 Reviewers agreed at journal 01 Apr, 2024 Reviewers invited by journal 01 Apr, 2024 Editor assigned by journal 01 Apr, 2024 Editor invited by journal 11 Mar, 2024 Submission checks completed at journal 11 Mar, 2024 First submitted to journal 28 Feb, 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. 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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-3998400","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":278272538,"identity":"4e59c436-c694-4cf6-9ba1-19f50fd9a096","order_by":0,"name":"Jia Su","email":"","orcid":"","institution":"Hebei University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Su","suffix":""},{"id":278272539,"identity":"b5705f3d-0ab5-4a58-adfa-fc838df300ea","order_by":1,"name":"Yichang Qin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIie3RsQrCMBCA4SuBdim6VgrtEwgpAaEgfZZIIbOjm3HxGRz6FkLnQCAu6d7Nujh10K2Dg8VJp8RNMP/+cXccgMv1g/kgTwPFS5SCFHZk4ikadWsWZDtF7UiCNJ51NzkFqbHlYr6imGIWe3t9b3sokjk3kVAKOt5CgrA55hWUZCFMJFJUjFNK79DUcQhiVRtJ2mecYrnl5/5qSUATGAkCoX1bothIGMq4InmFLW5JuVTe8Hi98tL2myIxko+i0PI17+Rb4XK5XH/RE1EzRs8NgcHKAAAAAElFTkSuQmCC","orcid":"","institution":"Hebei University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yichang","middleName":"","lastName":"Qin","suffix":""},{"id":278272540,"identity":"640532dc-18f9-4328-893c-bf77cd05e39f","order_by":2,"name":"Ze Jia","email":"","orcid":"","institution":"Hebei University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ze","middleName":"","lastName":"Jia","suffix":""},{"id":278272541,"identity":"cd6afb8f-ae39-4f41-817b-0541d4cd7676","order_by":3,"name":"Ben Liang","email":"","orcid":"","institution":"Hebei University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ben","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2024-02-29 03:45:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3998400/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3998400/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-68934-2","type":"published","date":"2024-08-01T15:58:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61793773,"identity":"77ef3b1e-752f-4ce8-af17-dbfbcb63dbd3","added_by":"auto","created_at":"2024-08-05 16:15:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":802134,"visible":true,"origin":"","legend":"","description":"","filename":"MPEYOLO.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3998400/v1_covered_ebad23d9-1dbe-4574-b79f-a272d73e5e16.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"MPE-YOLO: Enhanced Small Target Detection in Aerial Imaging","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Object detection, Aerial image, Small target, Model lightweight, YOLOv8","lastPublishedDoi":"10.21203/rs.3.rs-3998400/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3998400/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Aerial image target detection is crucial for urban planning, traffic monitoring, and disaster assessment, but existing detection algorithms face challenges in small target recognition and accuracy within complex environments. 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