Real-time Detection Algorithm of Aircraft Landing Gear based on improved YOLOv8

Real-time Detection Algorithm of Aircraft Landing Gear based on improved YOLOv8 | 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 Real-time Detection Algorithm of Aircraft Landing Gear based on improved YOLOv8 Ruizhen Gao, Meng Chen, Ziyue Zhao, Juan Ren This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4493909/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 This study innovatively improves the YOLOv8 target detection model, aiming to achieve fast and accurate detection of aircraft landing gear in natural environments. By introducing a small target detection layer, a dynamic serpentine convolutional layer, and a CoTattention mechanism, the study successfully optimized the original yolov8 model to effectively detect small-sized aircraft landing gears when presented at a distance. This paper introduces a small target detection layer of 160x160 on top of the original network, significantly improving the detection performance of airplane landing gear by fusing features from different layers. Dynamic serpentine convolution uses a dynamic structure and iterative strategy to improve the model's ability to perceive complex geometric structures by optimizing the convolution kernel. The CoTAttention mechanism allows the model to consider the information of each position in the input image more comprehensively. It significantly reduces the loss of contextual information by enhancing the ability to perceive small targets. The experimental findings demonstrate a noteworthy enhancement in the performance metrics, including precision, recall, and average accuracy, when comparing the enhanced model to its original counterpart. Furthermore, the improved model effectively meets the real-time detection requirements. Compared to other object detection models, the improved model performs, offering high accuracy and real-time detection capabilities, particularly demonstrating its versatility and practical value in detecting aircraft landing gear. airplane landing gear YOLOv8 small target detection dynamic snake convolution CoTAttention mechanism 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-4493909","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":311227119,"identity":"93b85001-ef12-484f-8cee-2bc3997eb7d0","order_by":0,"name":"Ruizhen Gao","email":"","orcid":"","institution":"Hebei University of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Ruizhen","middleName":"","lastName":"Gao","suffix":""},{"id":311227120,"identity":"697094a1-f824-44d7-a749-68a9823c9368","order_by":1,"name":"Meng Chen","email":"","orcid":"","institution":"Hebei University of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Chen","suffix":""},{"id":311227121,"identity":"7906eb93-efd2-413b-8187-67cb13384855","order_by":2,"name":"Ziyue Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYDACCSD++M9Gjp+Z+fADorUwzmBLM5ZsZ0szIFoLMwfb4cQN53kUJIjSIT+7+eFjBp60xM2HeRgMGGpsoglqYZxzzNi4QMLGeNth3gMPGI6l5TYQ0sIskWAmPcMgTXbbYb4EA8aGw4S1sEmkf5PmSTjMuLmZx0CCKC08Ejlm0jwHDituYCZWi4RETrHhzIY0Y4nDwEBOIMYv8jPSNz742ACMyv7Dhx98qLEhrAUVJJCmfBSMglEwCkYBLgAA3FY8TBpKzT8AAAAASUVORK5CYII=","orcid":"","institution":"Hebei University of Engineering","correspondingAuthor":true,"prefix":"","firstName":"Ziyue","middleName":"","lastName":"Zhao","suffix":""},{"id":311227122,"identity":"8d2923d8-09b6-45c9-b9f1-b54d1b62076c","order_by":3,"name":"Juan Ren","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2024-05-29 03:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4493909/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4493909/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66604047,"identity":"c46946b3-36e1-41e4-9c32-a69ebe84f155","added_by":"auto","created_at":"2024-10-14 17:47:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5461325,"visible":true,"origin":"","legend":"","description":"","filename":"RealtimeDetectionAlgorithmofAircraftLandingGearbasedonimprovedYOLOv8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4493909/v1_covered_16951364-ee73-461a-9e4f-dcb0ab6a3da6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Real-time Detection Algorithm of Aircraft Landing Gear based on improved YOLOv8","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":"airplane landing gear, YOLOv8, small target detection, dynamic snake convolution, CoTAttention mechanism","lastPublishedDoi":"10.21203/rs.3.rs-4493909/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4493909/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study innovatively improves the YOLOv8 target detection model, aiming to achieve fast and accurate detection of aircraft landing gear in natural environments. By introducing a small target detection layer, a dynamic serpentine convolutional layer, and a CoTattention mechanism, the study successfully optimized the original yolov8 model to effectively detect small-sized aircraft landing gears when presented at a distance. This paper introduces a small target detection layer of 160x160 on top of the original network, significantly improving the detection performance of airplane landing gear by fusing features from different layers. Dynamic serpentine convolution uses a dynamic structure and iterative strategy to improve the model's ability to perceive complex geometric structures by optimizing the convolution kernel. The CoTAttention mechanism allows the model to consider the information of each position in the input image more comprehensively. It significantly reduces the loss of contextual information by enhancing the ability to perceive small targets. 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