YOLO-DTO: Automotive door panel fastener detection algorithm based on deep learning

YOLO-DTO: Automotive door panel fastener detection algorithm based on deep learning | 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 YOLO-DTO: Automotive door panel fastener detection algorithm based on deep learning Wang Xiao-hui, Jia Yun-shuo, Guo Feng-Juan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4262014/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 The common detection of fasteners of automobile door panels is based on the template matching method, which has the problems of low detection accuracy and poor real-time performance under the influence of different lighting and different placement positions. To improve the detection speed and accuracy of fasteners in complex scenes, a small object detection algorithm YOLO-DTO (Detect Tiny Object) was proposed based on the YOLOv8 algorithm. Firstly, according to the characteristics of fasteners accounting for fewer image pixels, this paper reconstructs the early stage of the original algorithm by introducing the SPD (space-to-depth) module to retain more fine-grained information about fasteners, secondly, to enhance the algorithm's ability to pay attention to the context information of fasteners, the selective attention module is embedded in the Neck output position of the algorithm, and to optimize the regression efficiency of the bounding box, the CIOU loss function is replaced by the MPDIOU loss function. The experimental results show that the average detection accuracy of the YOLO-DTO algorithm is 98.8%, which is 9.1% and 1.7% higher than that of the template matching method and the YOLOv8 algorithm, respectively, which meets the detection standard of the factory production line and has practical value. automotive door panel fastener detection selective attention loss function deep learning 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. 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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-4262014","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292912075,"identity":"3a7ecd82-067b-408e-9a50-222ea08f4ab9","order_by":0,"name":"Wang Xiao-hui","email":"","orcid":"","institution":"North China Electric Power University","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Xiao-hui","suffix":""},{"id":292912076,"identity":"07019c70-7abf-492c-8676-a8689163ed71","order_by":1,"name":"Jia Yun-shuo","email":"","orcid":"","institution":"North China Electric Power University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Yun-shuo","suffix":""},{"id":292912077,"identity":"9c48ff02-8bf2-4f5c-969a-8e035ee258aa","order_by":2,"name":"Guo Feng-Juan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYDAC5gOJD2BsCeK0sCUkG5CshQ2ukjgt8m4Mz6oLamzyDA4wH7zNw2CXR1CL4TGGtNszjqUVGxxgS7bmYUguJqxlfkPabd6Gw4kbDvCYSfMwHEhsIKiljSGtmLfhP1AL/zfitMizMaQx8zYcANnCRpwWAzaGZGmeY8nFkofZjC3nGCQTYUsbT+Jnnhq7PL7jzQ9vvKmwI8KWAzwJIDqBgRnMJaQeZEsD+wGIllEwCkbBKBgFuAAAVzE4ipOp9Y4AAAAASUVORK5CYII=","orcid":"","institution":"North China Electric Power University","correspondingAuthor":true,"prefix":"","firstName":"Guo","middleName":"","lastName":"Feng-Juan","suffix":""}],"badges":[],"createdAt":"2024-04-13 13:58:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4262014/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4262014/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55954613,"identity":"007a4ea0-338b-4ba3-b1fd-77d0e7c88ba0","added_by":"auto","created_at":"2024-05-06 19:36:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":625042,"visible":true,"origin":"","legend":"","description":"","filename":"YOLODTO.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4262014/v1_covered_da97aab7-6a21-4734-8d01-ad933cebd5ce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"YOLO-DTO: Automotive door panel fastener detection algorithm based on deep learning","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":"automotive door panel fastener detection, selective attention, loss function, deep learning","lastPublishedDoi":"10.21203/rs.3.rs-4262014/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4262014/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe common detection of fasteners of automobile door panels is based on the template matching method, which has the problems of low detection accuracy and poor real-time performance under the influence of different lighting and different placement positions. To improve the detection speed and accuracy of fasteners in complex scenes, a small object detection algorithm YOLO-DTO (Detect Tiny Object) was proposed based on the YOLOv8 algorithm. Firstly, according to the characteristics of fasteners accounting for fewer image pixels, this paper reconstructs the early stage of the original algorithm by introducing the SPD (space-to-depth) module to retain more fine-grained information about fasteners, secondly, to enhance the algorithm's ability to pay attention to the context information of fasteners, the selective attention module is embedded in the Neck output position of the algorithm, and to optimize the regression efficiency of the bounding box, the CIOU loss function is replaced by the MPDIOU loss function. 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