FSS-YOLO: The Lightweight Drill Pipe Detection Method Based on YOLOv8n-obb

FSS-YOLO: The Lightweight Drill Pipe Detection Method Based on YOLOv8n-obb | 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 FSS-YOLO: The Lightweight Drill Pipe Detection Method Based on YOLOv8n-obb Mingyang Zhao, Xiaojun Li, Miao Li, Bangbang Mu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6045050/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 To achieve fast and accurate identification of drill pipes, we propose FSS-YOLO, which is a lightweight drill pipe detection method based on YOLOv8n-obb. This method first introduces the FasterBlock module into the C2f module of YOLOv8n-obb to reduce the number of model parameters and decrease the computational cost of the model and redundant feature maps. Next, the SimAM attention mechanism is added to the backbone network to enhance the weight of important features in the feature map and improves the model's feature extraction capability. In addition, using shared convolution to optimize the detection head, which not only lightens the detection head but also enhances its ability to learn features of different scales, improving the model's generalization ability. Finally, the FSS-YOLO algorithm is validated on the self-built dataset. The results show that compared with the original algorithm, FSS-YOLO achieves improvements of 5.1% in mAP50 and 11.5% in Recall, reduces the number of parameters by 45.8%, and achieves an inference speed of 27.8ms/frame on Jetson Orin NX. Additionally, the visual detection results for different scenarios demonstrate that the improved YOLOv8n-obb algorithm has promising application prospects Gas extraction YOLOv8n-obb SimAM Shared Conv Coal mine Intelligent coal mine 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-6045050","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":418018626,"identity":"2ab36c01-c6c5-4f7b-8fbd-8aef0c34568f","order_by":0,"name":"Mingyang Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIie3Rv0rEMBzA8YYfxCW9rCmoRXyBQEERiw6+yK8I16WbIDcGClldK4rv4OL8KwGn43yAW65vcIcgDiIXZ72cbg75QjLlQ/4lSSz2H1N+4KTc5wDkPialkNL8giym42K0wysS0/Fu1tF2wgbrqgcpDim1rtQGwyK/a4dF5YkFkZDiL0InxJarZjNh98+FxlldWEiJtJiLYzCQ3T5tJqDwSOH16Z6FERKquTgxxCENEK7qN4UcmD+Y9tvM/IxhIlTjd7EXB1+kN0jbiVLNlUb/yBw4uoQuRdb1bfAueVc/Du/+K/Mb517Z59m5lG2/XAXITzHzt/WxWCwW+9YaRGVShNUEm9QAAAAASUVORK5CYII=","orcid":"","institution":"Henan Polytechnic University","correspondingAuthor":true,"prefix":"","firstName":"Mingyang","middleName":"","lastName":"Zhao","suffix":""},{"id":418018628,"identity":"69d84335-b6df-4d3c-930c-42890a246703","order_by":1,"name":"Xiaojun Li","email":"","orcid":"","institution":"Henan Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Li","suffix":""},{"id":418018629,"identity":"e120d7be-98d9-4ce6-b431-e0888b0c118f","order_by":2,"name":"Miao Li","email":"","orcid":"","institution":"Henan Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Li","suffix":""},{"id":418018631,"identity":"3ad21243-3f11-4b65-9aca-f01014b21bc9","order_by":3,"name":"Bangbang Mu","email":"","orcid":"","institution":"Henan Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Bangbang","middleName":"","lastName":"Mu","suffix":""}],"badges":[],"createdAt":"2025-02-17 06:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6045050/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6045050/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77719571,"identity":"eb545340-92f9-49f3-a6b4-1d8864f6777a","added_by":"auto","created_at":"2025-03-04 15:47:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1665362,"visible":true,"origin":"","legend":"","description":"","filename":"FSSYOLO.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6045050/v1_covered_60939905-61c5-4ecb-a314-c428c8aabd17.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"FSS-YOLO: The Lightweight Drill Pipe Detection Method Based on YOLOv8n-obb","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":"Gas extraction, YOLOv8n-obb, SimAM, Shared Conv, Coal mine, Intelligent coal mine","lastPublishedDoi":"10.21203/rs.3.rs-6045050/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6045050/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo achieve fast and accurate identification of drill pipes, we propose FSS-YOLO, which is a lightweight drill pipe detection method based on YOLOv8n-obb. This method first introduces the FasterBlock module into the C2f module of YOLOv8n-obb to reduce the number of model parameters and decrease the computational cost of the model and redundant feature maps. Next, the SimAM attention mechanism is added to the backbone network to enhance the weight of important features in the feature map and improves the model's feature extraction capability. In addition, using shared convolution to optimize the detection head, which not only lightens the detection head but also enhances its ability to learn features of different scales, improving the model's generalization ability. Finally, the FSS-YOLO algorithm is validated on the self-built dataset. The results show that compared with the original algorithm, FSS-YOLO achieves improvements of 5.1% in mAP50 and 11.5% in Recall, reduces the number of parameters by 45.8%, and achieves an inference speed of 27.8ms/frame on Jetson Orin NX. 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