{"paper_id":"09ab787e-67e6-4db6-bd5a-398f8a451636","body_text":"Research on small target detection in aerial images based on Yolov7 and Transformer | 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 Research on small target detection in aerial images based on Yolov7 and Transformer xiaoli zhang, guocai zuo, le gao, yusi wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4235111/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 With the further development of the network, it has become more rare to obtain effective feature information of small targets in aerial images, and the resulting detection accuracy has become more challenging. To solve this problem, this paper combines the target detection algorithm YOLOv7, and introduces the neighborhood attention transformer (NAT) module based on it. A small target detection method based on Yolov7 and Transformer for aerial images is proposed. Compared with the traditional CNN module, the local feature Transformer at any two locations can interact and correlate, obtain more context information and capture more global information. By introducing location offset information, NAT also retains the advantages of visual Transformer, and further improves the effect of local feature extraction. In addition, considering the limited computing resources of edge computing devices, this paper adopts Light-weight Convolutional Encoder Network (LCEN) and introduces an embedded hierarchical attention network in the feature extraction stage. To balance the detection accuracy and inference speed of the model. The experimental data fully prove that the proposed method is effective in solving the problem of small target detection in aerial images. In addition, when compared with other mainstream aerial small target detection models on the two datasets, the proposed method shows higher average accuracy. It is especially worth mentioning that compared with the latest aerial scene target detection model TPH-YOLOv5, the average accuracy of the proposed method is improved by 7.34%. YOLOv7 Neighborhood attention Transformer LCEN 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. 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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-4235111\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":289077940,\"identity\":\"2d734ee8-1d75-4175-853b-6c68ca517a10\",\"order_by\":0,\"name\":\"xiaoli zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"xiaoli\",\"middleName\":\"\",\"lastName\":\"zhang\",\"suffix\":\"\"},{\"id\":289077941,\"identity\":\"bbe736d0-f710-4339-ac49-31082e821180\",\"order_by\":1,\"name\":\"guocai zuo\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDADNvYeECUHxAnEauE5A6KMJYjXwiCRQ6QW+RnJh1/zVNjJ80m+PfbwR4VBHT97jgHDj4ptOLUwzkhLs+Y5k2zYJp2XbsxzxkBCsueNAWPPmds4tTBL5JgZ87YdYGyTzjGTZmz7I2FwI8eAmbENtxY2qBb7NskzZpI/2wwk7Alp4ZHIMX4M1JLYJsFjJsEL1GIgQUCLBM+zNMY5Z5KT23iADgP6RXLGmWcFB/H5Rb49+fCHNxV2tvPbgQ4Dhhg/f3vyxgc/KnBrAXsHQ+gAPvVAwPyBgIJRMApGwSgY6QAAs/xMvIOTDEIAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"guocai\",\"middleName\":\"\",\"lastName\":\"zuo\",\"suffix\":\"\"},{\"id\":289077942,\"identity\":\"9ba59b58-e46e-4a78-92b4-2e52714d5516\",\"order_by\":2,\"name\":\"le gao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"le\",\"middleName\":\"\",\"lastName\":\"gao\",\"suffix\":\"\"},{\"id\":289077943,\"identity\":\"23e8eb91-0d23-437d-8b33-4f2034b5b85c\",\"order_by\":3,\"name\":\"yusi wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"yusi\",\"middleName\":\"\",\"lastName\":\"wang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-04-08 08:42:20\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4235111/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4235111/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":71619886,\"identity\":\"c5828d81-8b71-4dfd-a60e-a12018aa1347\",\"added_by\":\"auto\",\"created_at\":\"2024-12-17 08:02:11\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":474376,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ResearchonsmalltargetdetectioninaerialimagesbasedonYolov7andTransformer.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4235111/v1_covered_f4206867-dc23-48d1-a3d6-3b00194f63bb.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Research on small target detection in aerial images based on Yolov7 and Transformer\",\"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\":\"info@researchsquare.com\",\"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\":\"YOLOv7, Neighborhood attention, Transformer, LCEN\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4235111/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4235111/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eWith the further development of the network, it has become more rare to obtain effective feature information of small targets in aerial images, and the resulting detection accuracy has become more challenging. To solve this problem, this paper combines the target detection algorithm YOLOv7, and introduces the neighborhood attention transformer (NAT) module based on it. A small target detection method based on Yolov7 and Transformer for aerial images is proposed. Compared with the traditional CNN module, the local feature Transformer at any two locations can interact and correlate, obtain more context information and capture more global information. By introducing location offset information, NAT also retains the advantages of visual Transformer, and further improves the effect of local feature extraction. In addition, considering the limited computing resources of edge computing devices, this paper adopts Light-weight Convolutional Encoder Network (LCEN) and introduces an embedded hierarchical attention network in the feature extraction stage. To balance the detection accuracy and inference speed of the model. 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