FEPNet: A Feature Extraction-prediction Network for Coal Flotation Froth Image Segmentation

FEPNet: A Feature Extraction-prediction Network for Coal Flotation Froth Image Segmentation | 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 FEPNet: A Feature Extraction-prediction Network for Coal Flotation Froth Image Segmentation lingzhi liao, xianwu huang, heng zhang, haili shang, zhao cao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4942310/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 Coal flotation represents a flotation technique used to extract valuable mineral components from a coal slurry. The visual information on the coal slurry flotation froth's surface can both provide intuitive feedback on the flotation effect and guide the operator to adjust the flotation process parameters accordingly. However, the image segmentation of the coal flotation froth is challenging due to the problems of high noise, fuzzy boundaries, and strong adhesion. To address these problems, this paper proposes an innovative and effective segmentation model for coal flotation froth images named the Feature Extraction-Prediction Network (FEPNet). The FEPNet is a deep neural network model with an encoder-decoder structure, and it mainly consists of two parts, namely a feature extraction network and a prediction network.The network model is designed using the Stem Convolution Group module (SCG), Global-Local Hybrid Attention module (GLHA), and High-Width Combined Attention module (HWCA) to extract global and local feature information and realize multi-dimensional feature information fusion. The proposed FEPNet model is evaluated on a self-constructed image segmentation dataset of coal slurry flotation foams. Compared to the UNet model, the proposed model can improve the mIoU, DSC, and Acc metrics by 4.86, 2.86, and 3.48%, respectively. The experimental results show that the proposed FEPNet model can effectively address the segmentation difficulties of coal slurry flotation froth images. This study lays the foundation for optimizing the flotation process parameters and realizing the intelligence of flotation field parameters. In addition, generalization experiments are conducted on the skin lesion segmentation dataset, and the results show that the proposed FEPNet has excellent segmentation generalization ability.This paper provides a basis and reference for the automatic control of flotation process, and lays a foundation for improving production efficiency and product quality. coal flotation flotation froth image convolutional neural network image segmentation attention mechanism 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. 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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-4942310","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":343978950,"identity":"8c8a7865-5d5f-430a-8ff3-aa2fe17088b0","order_by":0,"name":"lingzhi liao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"lingzhi","middleName":"","lastName":"liao","suffix":""},{"id":343978951,"identity":"e926c804-b4e0-4a79-9de3-c11241a1030e","order_by":1,"name":"xianwu huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACAwglASYPfKiokWNjbz9AtBbGgzPOHDPm4zmTQIwWMGA+zNvGnDhPwsEAp3IQMJdIfvbwa5uFvLlEjsFhnjNs6W0SDAkMPyq24dRiOSPN3FjmjIThzhk5BgfnVMjktkk3HmDsOXMbt8NuJJhJS1RIMG64nWNw4M0Zttw2mQMJzIxt+LSkf5OWMJCwB2sB+iWdTSLBgICWHDPJDxUSiSAtB4FaEghrOfOmTJrhjETyhvvPCkCBbNgGDOSDeP1yPH2b5M+2OtsNZw5v/gCMSnn59vaDD35U4NYCAsw86CIH8KoHAsYfhFSMglEwCkbByAYAcPlemo62rKEAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"xianwu","middleName":"","lastName":"huang","suffix":""},{"id":343978952,"identity":"2b49609b-9178-480e-9fb0-37e045b8ded4","order_by":2,"name":"heng zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"heng","middleName":"","lastName":"zhang","suffix":""},{"id":343978953,"identity":"20ca5a41-75be-42b2-8009-c35218636846","order_by":3,"name":"haili shang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"haili","middleName":"","lastName":"shang","suffix":""},{"id":343978954,"identity":"e5ef5fe1-9e79-4f1a-bda0-1b37567574c0","order_by":4,"name":"zhao cao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"zhao","middleName":"","lastName":"cao","suffix":""},{"id":343978955,"identity":"02912b51-3914-4ca5-bc18-94cf6357c7e0","order_by":5,"name":"jinshan zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"jinshan","middleName":"","lastName":"zhang","suffix":""}],"badges":[],"createdAt":"2024-08-20 05:50:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4942310/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4942310/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67714150,"identity":"8a3dfb86-cb11-4b4d-8a9c-577ede16cbb4","added_by":"auto","created_at":"2024-10-29 03:23:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1159997,"visible":true,"origin":"","legend":"","description":"","filename":"FEPNet.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4942310/v1_covered_99b6558b-b1ef-4c34-9ca6-35400a558208.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"FEPNet: A Feature Extraction-prediction Network for Coal Flotation Froth Image Segmentation","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":"coal flotation, flotation froth image, convolutional neural network, image segmentation, attention mechanism","lastPublishedDoi":"10.21203/rs.3.rs-4942310/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4942310/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoal flotation represents a flotation technique used to extract valuable mineral components from a coal slurry. The visual information on the coal slurry flotation froth's surface can both provide intuitive feedback on the flotation effect and guide the operator to adjust the flotation process parameters accordingly. However, the image segmentation of the coal flotation froth is challenging due to the problems of high noise, fuzzy boundaries, and strong adhesion. To address these problems, this paper proposes an innovative and effective segmentation model for coal flotation froth images named the Feature Extraction-Prediction Network (FEPNet). The FEPNet is a deep neural network model with an encoder-decoder structure, and it mainly consists of two parts, namely a feature extraction network and a prediction network.The network model is designed using the Stem Convolution Group module (SCG), Global-Local Hybrid Attention module (GLHA), and High-Width Combined Attention module (HWCA) to extract global and local feature information and realize multi-dimensional feature information fusion. 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In addition, generalization experiments are conducted on the skin lesion segmentation dataset, and the results show that the proposed FEPNet has excellent segmentation generalization ability.This paper provides a basis and reference for the automatic control of flotation process, and lays a foundation for improving production efficiency and product quality.\u003c/p\u003e","manuscriptTitle":"FEPNet: A Feature Extraction-prediction Network for Coal Flotation Froth Image Segmentation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-19 10:13:14","doi":"10.21203/rs.3.rs-4942310/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"f2d0cb33-e9b3-4ec9-a1c7-15829635b4d4","owner":[],"postedDate":"September 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-29T03:23:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-19 10:13:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4942310","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4942310","identity":"rs-4942310","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}