A method of SF 6 gas leakage detection for infrared imaging based on Infrared Patch-Image model

A method of SF 6 gas leakage detection for infrared imaging based on Infrared Patch-Image model | 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 SF 6 gas leakage detection for infrared imaging based on Infrared Patch-Image model Quan Lu, Lifeng Huang, Likun Hu, Zhuangding Han This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1714885/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 Due to the low contrast and low signal-to-noise ratio (SNR) of infrared imaging of SF 6 (Sulfur Hexafluoride) gas, it is susceptible to environmental noise, resulting in low accuracy and a high false alarm rate for existing detection algorithms. We propose a single-frame detection method for infrared imaging SF 6 gas leakage based on the IPI model. Due to the robust principal component analysis (RPCA) limitation in describing complex backgrounds, this method uses weighted nuclear norm minimization (WNNM) to better describe the background's low-rank characteristics and then solve the problem using the Accelerating Proximal Gradient (APG) algorithm. Finally, it is possible to accurately extract SF 6 gas leakage points from the sparse target image using adaptive threshold segmentation. We conducted experiments on both indoor and outdoor SF 6 gas leakage tracks. The results demonstrate that the algorithm can effectively reduce the false noise generated by the complex background edge under indoor experimental conditions with SF 6 gas leakage of 0.04ml/min and a distance of 5m, while also improving leak detection accuracy. Infrared small target detection SF6 gas leakage detection low-contrast detection In-frared patch-image model Weighted nuclear norm minimization 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-1714885","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":111300577,"identity":"deab1377-1f33-486e-8175-6200c7c585e5","order_by":0,"name":"Quan Lu","email":"","orcid":"","institution":"Guangxi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Quan","middleName":"","lastName":"Lu","suffix":""},{"id":111300578,"identity":"24f45038-d55b-41c5-86b1-2927d4c57307","order_by":1,"name":"Lifeng Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYNCCHzb8QJLxQUJFDZE6GHvSJBsYGJgNHpw5RqwtbIdBWtgkH7YwE1YsP7v54YcPPIcl+KXbr1UkNrAx8Ld3J+DVYnDnmLHkDIt0Cck5Z8puJO6QYZA4c3YDfi0SCWbMPDzWdQY3ctJuJJ5hA4rk4tciPyP9G/MfNmYJe6CWgsQ2ZsJaGG7kmDEzsDlLGEikH2MgSovBnTPFkr09aRISN3KYJRLOHOMh6Bf52e0bP/z4YSPBPyP94ccfFTVy/O29BBwmAWfxGIBJ/MpRtbA/IKx6FIyCUTAKRiQAAONYST56PpkcAAAAAElFTkSuQmCC","orcid":"","institution":"Guangxi University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Lifeng","middleName":"","lastName":"Huang","suffix":""},{"id":111300580,"identity":"560297db-2beb-4b77-8c7e-1e8199f012d7","order_by":2,"name":"Likun Hu","email":"","orcid":"","institution":"Guangxi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Likun","middleName":"","lastName":"Hu","suffix":""},{"id":111300581,"identity":"c7e3561d-5f59-4cc9-8af2-247594e8c504","order_by":3,"name":"Zhuangding Han","email":"","orcid":"","institution":"Guangxi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhuangding","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2022-06-01 08:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1714885/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1714885/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22412667,"identity":"e148ea81-5390-4dab-97a0-eeb324a3c77b","added_by":"auto","created_at":"2022-06-08 15:03:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":454907,"visible":true,"origin":"","legend":"","description":"","filename":"AmethodofSF6gasleakagedetectionforinfraredimagingbasedonInfraredPatchImagemodel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1714885/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A method of SF 6 gas leakage detection for infrared imaging based on Infrared Patch-Image model","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1714885/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":false,"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":"Infrared small target detection, SF6 gas leakage detection, low-contrast detection, In-frared patch-image model, Weighted nuclear norm minimization","lastPublishedDoi":"10.21203/rs.3.rs-1714885/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1714885/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDue to the low contrast and low signal-to-noise ratio (SNR) of infrared imaging of SF\u003csub\u003e6\u003c/sub\u003e (Sulfur Hexafluoride) gas, it is susceptible to environmental noise, resulting in low accuracy and a high false alarm rate for existing detection algorithms. We propose a single-frame detection method for infrared imaging SF\u003csub\u003e6\u003c/sub\u003e gas leakage based on the IPI model. Due to the robust principal component analysis (RPCA) limitation in describing complex backgrounds, this method uses weighted nuclear norm minimization (WNNM) to better describe the background's low-rank characteristics and then solve the problem using the Accelerating Proximal Gradient (APG) algorithm. Finally, it is possible to accurately extract SF\u003csub\u003e6\u003c/sub\u003e gas leakage points from the sparse target image using adaptive threshold segmentation. We conducted experiments on both indoor and outdoor SF\u003csub\u003e6\u003c/sub\u003e gas leakage tracks. The results demonstrate that the algorithm can effectively reduce the false noise generated by the complex background edge under indoor experimental conditions with SF\u003csub\u003e6\u003c/sub\u003e gas leakage of 0.04ml/min and a distance of 5m, while also improving leak detection accuracy.\u003c/p\u003e","manuscriptTitle":"A method of SF 6 gas leakage detection for infrared imaging based on Infrared Patch-Image model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-08 15:03:00","doi":"10.21203/rs.3.rs-1714885/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":"802f45f3-26bb-4d25-8ad4-1f786bc2420b","owner":[],"postedDate":"June 8th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-03-08T17:44:22+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-08 15:03:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1714885","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1714885","identity":"rs-1714885","version":["v1"]},"buildId":"ApUGefWb6u5IBVtyqm6d5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}