A method of dense occlusion juvenile abalone segmentation based on improved coordinate attention mechanism

A method of dense occlusion juvenile abalone segmentation based on improved coordinate attention mechanism | 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 A method of dense occlusion juvenile abalone segmentation based on improved coordinate attention mechanism Jun Yue, Changyi Dai, Guangjie Kou, Zihao Mao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7846698/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The detection and classification of juvenile abalone is a key link in the abalone breeding industry, and juvenile abalone are small, densely distributed and prone to complex occlusion, which makes the efficiency of manual measurement of biomass low and error large. Based on the framework of deep learning network, this paper proposes a method of dense juvenile abalone segmentation with improved coordinate attention mechanism, and realizes the high-efficiency non-contact segmentation detection of dense juvenile abalone in complex situations. The improved shape feature attention mechanism designed by the algorithm model makes the network pay more attention to the details of juvenile abalone by emphasizing or suppressing the importance of features. At the same time, the algorithm model also constructs a Transformer module embedded with a double-layer Gaussian prior encoder, which improves the accuracy of juvenile abalone feature prediction by replacing the feedforward network with a Gaussian prior. Finally, the segmentation data set of juvenile abalone (JASD) is established and the effectiveness of the proposed algorithm model is verified by experiments. Deep learning Shape feature attention Dense small target Target segmentation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Oct, 2025 Reviews received at journal 23 Oct, 2025 Reviews received at journal 22 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers invited by journal 16 Oct, 2025 Editor assigned by journal 16 Oct, 2025 Submission checks completed at journal 13 Oct, 2025 First submitted to journal 13 Oct, 2025 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-7846698","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":537141079,"identity":"edb23634-b316-4cd0-8b22-353615950ddc","order_by":0,"name":"Jun Yue","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Yue","suffix":""},{"id":537141080,"identity":"8422b7d2-c19f-48ba-8926-4b9beccd0d0c","order_by":1,"name":"Changyi Dai","email":"","orcid":"","institution":"Ludong 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