A Multi-Scale Coupled Relationship Activation-Based Few-Shot Detection Method for Endangered Wildlife

A Multi-Scale Coupled Relationship Activation-Based Few-Shot Detection Method for Endangered Wildlife | 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 Multi-Scale Coupled Relationship Activation-Based Few-Shot Detection Method for Endangered Wildlife Wen Feng, Congjun Cao, Yunlong Shao, Xusheng Wang, Wu Xiong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4538262/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract In the domain of monitoring and conservation of rare and endangered wildlife, traditional object detection methods fail to operate effectively due to overfitting caused by limited image data. To address this issue, this paper proposes a small-sample object detection method that utilizes limited support information to achieve accurate detection of endangered wildlife images. Firstly, we introduce the Feature Pyramid Network (FPN) to adapt to foreground targets at various scales. Secondly, we propose a Coupled Relationship Activation (CRA) module that aggregates features from the support set into a comprehensive feature vector and utilizes it to activate similar regions in the query feature. Furthermore, to balance the differences between support and query features, we incorporate cosine similarity and short-circuit structures before and after the activation process, thereby mitigating the impact of activating dissimilar features erroneously. During the above process, we construct a balanced dataset, CRBAD, which comprises images of both endangered and common animal categories, and employ it to conduct practical testing of the proposed method. Experimental results demonstrate that the proposed model achieves state-of-the-art performance in detecting endangered wildlife images. Deep learning Few-Shot Object Detection Wild animal detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 15 Jul, 2024 Submission checks completed at journal 13 Jun, 2024 First submitted to journal 06 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. 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-4538262","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313949919,"identity":"fe8ec626-b35a-414c-b7f7-675c3a3460ce","order_by":0,"name":"Wen Feng","email":"","orcid":"","institution":"Xi'an University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Feng","suffix":""},{"id":313949920,"identity":"8abc6df0-0c13-4b76-9e7d-2cd5843b60a4","order_by":1,"name":"Congjun Cao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBADZiA6AGUnEK2FDaTUgHgtQMBjQJwW/vbDBz983FHLbnC755vEz7Y/DPzsOQYMP3fg1iJxJi1ZcuaZ48wGd85uNuxtM2CQ7HljwNh7BrcWA4YcM2betmPMBjdyNz7gBWoxuJFjwMzYhkcL/xuYlpwHB/8CtdgT1CIBtqUGpIXxMdgWCQJaJG48A/ql7QCz5I00Y2OZc8Y8EmeeFRzsxaOFvz8ZGGJtdcl8N5KfSb4pk5Pjb0/e+OAnHi1QcDgZxuIBEQcIamBgqLMjQtEoGAWjYBSMVAAAPyJN+rQwZ0wAAAAASUVORK5CYII=","orcid":"","institution":"Xi'an University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Congjun","middleName":"","lastName":"Cao","suffix":""},{"id":313949921,"identity":"2b9df58b-431d-4464-8458-37bd03e328e2","order_by":2,"name":"Yunlong Shao","email":"","orcid":"","institution":"Xi'an University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yunlong","middleName":"","lastName":"Shao","suffix":""},{"id":313949922,"identity":"38ec33ea-7dca-4943-8cd6-6892b88996d9","order_by":3,"name":"Xusheng Wang","email":"","orcid":"","institution":"Xi'an University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xusheng","middleName":"","lastName":"Wang","suffix":""},{"id":313949923,"identity":"3657b138-c19f-45ca-bc29-508f2f97b5ae","order_by":4,"name":"Wu Xiong","email":"","orcid":"","institution":"Xi'an University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Wu","middleName":"","lastName":"Xiong","suffix":""}],"badges":[],"createdAt":"2024-06-06 07:51:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4538262/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4538262/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59232155,"identity":"b921c422-0894-46b6-b8a5-6c562cb8fff6","added_by":"auto","created_at":"2024-06-28 02:55:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":714958,"visible":true,"origin":"","legend":"","description":"","filename":"revisedmanuscriptAMultiScaleCoupledRelationshipActivationBasedFewShotDetectionMethodforEndangeredWildlife.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4538262/v1_covered_72eb7942-eb0b-4ff5-9e66-ca3cfff77c1f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multi-Scale Coupled Relationship Activation-Based Few-Shot Detection Method for Endangered Wildlife","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"international-journal-of-multimedia-information-retrieval","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mmir","sideBox":"Learn more about [International Journal of Multimedia Information Retrieval](http://link.springer.com/journal/13735)","snPcode":"13735","submissionUrl":"https://submission.nature.com/new-submission/13735/3","title":"International Journal of Multimedia Information Retrieval","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Deep learning, Few-Shot Object Detection, Wild animal detection","lastPublishedDoi":"10.21203/rs.3.rs-4538262/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4538262/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e In the domain of monitoring and conservation of rare and endangered wildlife, traditional object detection methods fail to operate effectively due to overfitting caused by limited image data. To address this issue, this paper proposes a small-sample object detection method that utilizes limited support information to achieve accurate detection of endangered wildlife images. Firstly, we introduce the Feature Pyramid Network (FPN) to adapt to foreground targets at various scales. Secondly, we propose a Coupled Relationship Activation (CRA) module that aggregates features from the support set into a comprehensive feature vector and utilizes it to activate similar regions in the query feature. Furthermore, to balance the differences between support and query features, we incorporate cosine similarity and short-circuit structures before and after the activation process, thereby mitigating the impact of activating dissimilar features erroneously. During the above process, we construct a balanced dataset, CRBAD, which comprises images of both endangered and common animal categories, and employ it to conduct practical testing of the proposed method. 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