RTDETR-Refa: a real-time detection method for multi-breed classiffcation of cattle 

RTDETR-Refa: a real-time detection method for multi-breed classiffcation of cattle | 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 RTDETR-Refa: a real-time detection method for multi-breed classiffcation of cattle Bingxuan Li, Jiandong Fang, Yvdong Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4579443/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jan, 2025 Read the published version in Journal of Real-Time Image Processing → Version 1 posted 11 You are reading this latest preprint version Abstract In the farming industry in order to cope with the problems such as the complex environment of the pasture and dense targets, etc., which lead to increased difficulty in recognition, so as to quickly classify and automatically identify cattle breeds and improve the accuracy. In this paper, an RTDETR-Refa (Rep- Conv Efficient Faster Attention) model is proposed for classification and identification of cattle breeds. Firstly, some novel improvements have been made to ResNet18. Replacing the 1×1 convolution in Faster-Block with the reparameterised RepConv, and adding the Efficient Multiscale Attention Module (EMA) in front of the global average pooling layer in Faster-Block to enhance the transformation and classification of features, Finally, the 4-layer BasicBlock after the 3 convolutional layers in the resnet18-backbone is replaced by the improved Faster-Block.Finally, the results of the training tests of the RTDETR-Refa model are compared with other classical models: the YOLO family of models: the YOLOv5m, YOLOv6, YOLOv8m, YOLOv9 and the state-of-the-art (SOTA) CNN backbone networks EfficientViT, FasterNet, UniRepLKNet, TransNeXt verifying their superiority. The average accuracy of the RTDETR-Refa model on the cattle classification training set is 91.6%, which is 0.8% higher than that of ResNet18 and 0.9-5.2% higher than that of other classical models. The experimental results show that the RTDETR-Refa model proposed in this paper is capable of identifying and classifying cattle of different breeds while ensuring similar detection speeds, demonstrating the feasibility of convolutional neural networks in breed identification and classification. RT-DETR classification model Classification of varieties TIDE Practical Scenario Application Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Jan, 2025 Read the published version in Journal of Real-Time Image Processing → Version 1 posted Editorial decision: Revision requested 30 Sep, 2024 Reviews received at journal 25 Aug, 2024 Reviews received at journal 29 Jul, 2024 Reviewers agreed at journal 29 Jul, 2024 Reviewers agreed at journal 29 Jul, 2024 Reviews received at journal 08 Jul, 2024 Reviewers agreed at journal 18 Jun, 2024 Reviewers invited by journal 18 Jun, 2024 Editor assigned by journal 18 Jun, 2024 Submission checks completed at journal 18 Jun, 2024 First submitted to journal 14 Jun, 2024 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-4579443","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321751770,"identity":"d21ff931-68a2-4139-bcfc-25bd9aefceca","order_by":0,"name":"Bingxuan Li","email":"","orcid":"","institution":"Inner Mongolia University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Bingxuan","middleName":"","lastName":"Li","suffix":""},{"id":321751771,"identity":"465bf8ea-4f05-486e-8408-d7277915aa49","order_by":1,"name":"Jiandong Fang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBAC9gYgIcFmwwPhshGhhecAWEsaUAszKVoY2A4zkKCF/ezhFxZl52V0Z+QfYPhQdpiBf3YDAS08eWkWEudu85jdSGZgnHHuMIPEnQP4tdgz5JgZSLZBtDDzth1mMJBIIGAL/xuQlnMQLX+J0iKRY/xAsu0ARAsjcVremDFInEvmMTvz2OBgz7l0HokbBB2WY/xZoszO3ux44sMHP8qs5fhnENACBGzSElDWAZAZBNUDAfPHD8QoGwWjYBSMgpELAH+fPYPGGLU1AAAAAElFTkSuQmCC","orcid":"","institution":"Inner Mongolia University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Jiandong","middleName":"","lastName":"Fang","suffix":""},{"id":321751772,"identity":"f8f87ce3-3885-4950-a0c5-2554769bdd47","order_by":2,"name":"Yvdong Zhao","email":"","orcid":"","institution":"Inner Mongolia University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yvdong","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-06-14 04:52:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4579443/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4579443/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11554-024-01613-7","type":"published","date":"2025-01-04T15:57:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73093210,"identity":"5b6cecc3-56f3-4108-8d4b-f044842fcb0f","added_by":"auto","created_at":"2025-01-06 16:10:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3590741,"visible":true,"origin":"","legend":"","description":"","filename":"RTDETRRefaarealtimedetectionmethodformultibreedclassificationofcattle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4579443/v1_covered_ab576c11-2980-4cad-b6d9-0fc98c607f16.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"RTDETR-Refa: a real-time detection method for multi-breed classiffcation of cattle ","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-real-time-image-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rtip","sideBox":"Learn more about [Journal of Real-Time Image Processing](http://link.springer.com/journal/11554)","snPcode":"11554","submissionUrl":"https://submission.nature.com/new-submission/11554/3","title":"Journal of Real-Time Image Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"RT-DETR classification model, Classification of varieties, TIDE, Practical Scenario Application","lastPublishedDoi":"10.21203/rs.3.rs-4579443/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4579443/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the farming industry in order to cope with the problems such as the complex environment of the pasture and dense targets, etc., which lead to increased difficulty in recognition, so as to quickly classify and automatically identify cattle breeds and improve the accuracy. In this paper, an RTDETR-Refa (Rep- Conv Efficient Faster Attention) model is proposed for classification and identification of cattle breeds. Firstly, some novel improvements have been made to ResNet18. Replacing the 1×1 convolution in Faster-Block with the reparameterised RepConv, and adding the Efficient Multiscale Attention Module (EMA) in front of the global average pooling layer in Faster-Block to enhance the transformation and classification of features, Finally, the 4-layer BasicBlock after the 3 convolutional layers in the resnet18-backbone is replaced by the improved Faster-Block.Finally, the results of the training tests of the RTDETR-Refa model are compared with other classical models: the YOLO family of models: the YOLOv5m, YOLOv6, YOLOv8m, YOLOv9 and the state-of-the-art (SOTA) CNN backbone networks EfficientViT, FasterNet, UniRepLKNet, TransNeXt verifying their superiority. 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