A deep learning framework combining graph neural networks and YOLO for military target detection in urban battlefields

A deep learning framework combining graph neural networks and YOLO for military target detection in urban battlefields | 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 A deep learning framework combining graph neural networks and YOLO for military target detection in urban battlefields Xiaoyu Wang, Lijuan Zhang, Yutong Jiang, Hui Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4819035/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 In the urban battlefield environment, the rapid movement and frequent occlusion of military targets often lead to low detection accuracy during target recognition, and the existing models are difficult to meet the real-time requirements of battlefield recognition. To address this problem, this study proposes an innovative method that combines graph neural networks and YOLO models to solve the problem of low detection accuracy caused by slow detection speed and fuzziness of existing models. First, we improved the YOLOv7 model by introducing the SPPFPC structure, CARAFE structure and DSConv. These improvements not only reduced the complexity of the model and achieved lightweight, but also optimized the regression box by using the latest Shape-IoU method, effectively reducing the positioning loss of the model. Then, the model performance was improved by introducing intelligent reasoning and optimization processes in the model output stage, so that the model can re-reason the confidence of objects based on the spatial position relationship between objects. We constructed a graph relationship model based on the detection results and input it into the adjusted SeHGNN network. The SeHGNN network is responsible for learning the complex relationship between targets and recalculating the confidence. Through experimental verification, the improved model shows significant performance improvement on [email protected] , proving the effectiveness of this method.Through this method that combines traditional target detection technology with the knowledge reasoning function of graph neural networks, the model's detection performance for military targets in urban battlefields is significantly improved. target detection graph neural network YOLOv7 visual reasoning shape-iou 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. 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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-4819035","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":344486809,"identity":"c1d9cae3-a9c8-45e4-952f-b568d50b526d","order_by":0,"name":"Xiaoyu Wang","email":"","orcid":"","institution":"Changchun University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Wang","suffix":""},{"id":344486810,"identity":"ad2c18e3-f206-4564-8aed-73f0153035ff","order_by":1,"name":"Lijuan Zhang","email":"","orcid":"","institution":"Wuxi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Zhang","suffix":""},{"id":344486811,"identity":"8e695dc7-c730-4276-b15c-96def770c50d","order_by":2,"name":"Yutong Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBACxmYk9oMEAxsefv4G4rRIADGzwYeCNBnJGQeIsw2khU1yxofDNgYNCfiVMrczP3v4te1wHb90+wVpHoPzPAYMBxg/fMzB5zA2c2PZtsMSknPOFBjzGNzmMWduYJacuQ2vX8ykJYFaDG7kJCSDtFg2HGBj5sWrhf0bXMthHoNzPAYHEghp4TGT/AjWkn6wcYbBAaK0lEkznEuXnDkjh5nhg0Eyj+SMg814/WLYf3yb5I8ya35+ifTnPxL+2Nnz8zcf/PARn5YGYEDzsoGYwOCF2tyAWz0QyIOU/PgDYrI/wKtyFIyCUTAKRi4AAJwAUdH3LYKmAAAAAElFTkSuQmCC","orcid":"","institution":"China North Vehicle Research Institute","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yutong","middleName":"","lastName":"Jiang","suffix":""},{"id":344486813,"identity":"a62f1404-6124-492f-b178-77b61f59787b","order_by":3,"name":"Hui Zhao","email":"","orcid":"","institution":"Changchun University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-07-29 04:41:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4819035/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4819035/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64926119,"identity":"4e7dd9fd-b52d-459e-b932-1f6b4e5ee8ac","added_by":"auto","created_at":"2024-09-20 12:52:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":42817423,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXTemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4819035/v1_covered_ee960a3a-8e9c-401f-8cd1-32ecd8703a36.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A deep learning framework combining graph neural networks and YOLO for military target detection in urban battlefields","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"target detection, graph neural network, YOLOv7, visual reasoning, shape-iou","lastPublishedDoi":"10.21203/rs.3.rs-4819035/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4819035/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the urban battlefield environment, the rapid movement and frequent occlusion of military targets often lead to low detection accuracy during target recognition, and the existing models are difficult to meet the real-time requirements of battlefield recognition. To address this problem, this study proposes an innovative method that combines graph neural networks and YOLO models to solve the problem of low detection accuracy caused by slow detection speed and fuzziness of existing models. First, we improved the YOLOv7 model by introducing the SPPFPC structure, CARAFE structure and DSConv. These improvements not only reduced the complexity of the model and achieved lightweight, but also optimized the regression box by using the latest Shape-IoU method, effectively reducing the positioning loss of the model. Then, the model performance was improved by introducing intelligent reasoning and optimization processes in the model output stage, so that the model can re-reason the confidence of objects based on the spatial position relationship between objects. We constructed a graph relationship model based on the detection results and input it into the adjusted SeHGNN network. 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