Camouflaged Object Detection Based on Deformable Convolution and Edge Guidance

Camouflaged Object Detection Based on Deformable Convolution and Edge Guidance | 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 Camouflaged Object Detection Based on Deformable Convolution and Edge Guidance Xu Wang, Chong Chen, Kang Ruan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5259126/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 Camouflaged Object Detection (COD), which aims to accurately segment objects that perfectly blend into the surrounding environment, is a complex task. Although many deep learning-based COD methods have emerged, they are still unable to effectively segment camouflaged objects from the background in a complete and fine-grained manner. Based on this, we will propose a novel network DCEGNet (Deformable Convolutional and Edge-Guided Networks) for camouflaged object detection using deformable convolution as well as edge-attached semantic features. Specifically, in order to address the situation that complex objects are difficult to be segmented effectively, we design a deformable convolution based on a self-attention mechanism to change the shape of the convolution kernel, on top of which we also propose an Edge Detection Module (EDM) to explore the additional edge semantic information to guide the feature learning of the COD, which enables the model to utilize the boundary information to accurately locate and segment the object. Extensive experiments on three COD benchmark datasets show that our DCEGNet significantly outperforms 20 existing methods on four widely used evaluation metrics. Camouflaged Object Detection Deformable Convolutiona Edge Detection Module 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-5259126","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":366408731,"identity":"af42fe50-0aba-4568-9e7b-1ea9a664977a","order_by":0,"name":"Xu Wang","email":"","orcid":"","institution":"Anhui Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Wang","suffix":""},{"id":366408733,"identity":"c5107e1b-69da-49b3-84d5-b968cc212e32","order_by":1,"name":"Chong Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACCQY2BsYGhgR+KB/IJlaLZANENQlaDA4Qq0V+dvOzhz932OUZ30h//uAHg43shgPMzx7g02Jw55i5Me+Z5GKzGzmGjT0MacYbDrCZG+DVIpHDJs3YdiBx240cxmYGhsOJGw7wsEngddiMHDbJn0Atm2ekPwRq+U9YC8ONHDYJXqCWDRIJhkAtBwhrMbiRZibN25acOOPMG8OZPQbJxjMPs5kRcFjyM6DD7BL729MffPhRYSfbd7z5GX6HoVkKxMwkqB8Fo2AUjIJRgB0AAEejTDoedLdzAAAAAElFTkSuQmCC","orcid":"","institution":"Anhui Jianzhu University","correspondingAuthor":true,"prefix":"","firstName":"Chong","middleName":"","lastName":"Chen","suffix":""},{"id":366408736,"identity":"d084d83f-770a-4f02-b0f0-23abf56b52fe","order_by":2,"name":"Kang Ruan","email":"","orcid":"","institution":"Anhui Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"Kang","middleName":"","lastName":"Ruan","suffix":""}],"badges":[],"createdAt":"2024-10-14 07:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5259126/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5259126/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68223790,"identity":"fce42ceb-bff7-4776-a51e-6ddcbe3575f7","added_by":"auto","created_at":"2024-11-05 02:38:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1047405,"visible":true,"origin":"","legend":"","description":"","filename":"InternationalJournalofMachineLearningandCybernetics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5259126/v1_covered_837e3dbf-fb84-4348-b1e6-0062d59f6820.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Camouflaged Object Detection Based on Deformable Convolution and Edge Guidance","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":"Camouflaged Object Detection, Deformable Convolutiona, Edge Detection Module","lastPublishedDoi":"10.21203/rs.3.rs-5259126/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5259126/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Camouflaged Object Detection (COD), which aims to accurately segment objects that perfectly blend into the surrounding environment, is a complex task. 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