Multi-scene pipeline leakage video detection based on C3D-RF with TRCE loss joint supervision method

Multi-scene pipeline leakage video detection based on C3D-RF with TRCE loss joint supervision method | 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 Multi-scene pipeline leakage video detection based on C3D-RF with TRCE loss joint supervision method Chengang Lyu, Lijuan Wang, Mengqi Zhang, Xiaojiao Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4225679/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 In the field of industrial visual monitoring, PTZ cameras need to monitor the leakage of different easy leakage points. The backgrounds of the monitoring images change greatly and the characteristics of leakage targets are small, which will lead to large intra-class differences and small inter-class differences in the dataset samples. These problems affect the ability of the detection network to learn different class features of the leakage images and restrict the leakage detection performance. To solve the above problems, this paper proposes a pipeline leakage video detection method based on loss joint supervision in multiple scenes. Firstly, C3D network is used to simultaneously extract the spatial and temporal characteristics of the pipeline leakage video. In addition, the random forest classifier is used to avoid the tedious gradient calculation operation in the training process. Finally, by adding triple loss and center loss to jointly supervise model training, we measure the similarity within classes and the difference between classes to improve the decision-making ability of the leakage detection classifier. The experiment result shows that our method has a detection accuracy of 96.72% for pipeline leakage video in industrial environment and the training time is shortened by 18.75%. pipeline leakage detection C3D network metric learning random forest computer vision 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-4225679","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288196161,"identity":"6a1a084b-e4d3-4df2-b517-3d943605ee50","order_by":0,"name":"Chengang Lyu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACPgYGNiiT+QCDBIg+QEALG0ILW2IDqVp4DBsYiNIikfzswccdtfLm/Gu+P7BsY5Dju5HA+LkAr5Y0c8OZZ44b7pzxdmODZBuDseSNBGbpGfi08Jxhk+ZtO8a44cZZsJbEDTcS2Jh5CGn523bMfsONMw9BWuoJa2HvYZNmbKtJ3HC+hxGkJcGAsJY2M8netgPJG26wGc6QOCcB9NjDZml8WviZmZ9J/Gyrs91w/vCDzxJlNvJ8x5MPfsanBQoOMzBIJDAwS4Djn7GBsAYGhjqgfQcYGD8Qo3YUjIJRMApGHAAAg3FMyMx8Y3QAAAAASUVORK5CYII=","orcid":"","institution":"Tianjin University","correspondingAuthor":true,"prefix":"","firstName":"Chengang","middleName":"","lastName":"Lyu","suffix":""},{"id":288196162,"identity":"7619584b-24f2-43a5-8865-d0192e5be5e6","order_by":1,"name":"Lijuan Wang","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Wang","suffix":""},{"id":288196163,"identity":"a940763e-d45b-40db-9872-ddaec77b838a","order_by":2,"name":"Mengqi Zhang","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Mengqi","middleName":"","lastName":"Zhang","suffix":""},{"id":288196164,"identity":"154b3373-5818-4e2d-80e2-8683fb715de6","order_by":3,"name":"Xiaojiao Lin","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojiao","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-04-06 04:59:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4225679/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4225679/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100586816,"identity":"c097ca35-60f5-416c-aa54-15717044c197","added_by":"auto","created_at":"2026-01-19 12:08:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3361473,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4225679/v1_covered_45910149-3127-4a4c-bbb9-bd0343e62418.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-scene pipeline leakage video detection based on C3D-RF with TRCE loss joint supervision method","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":"pipeline leakage detection, C3D network, metric learning, random forest, computer vision","lastPublishedDoi":"10.21203/rs.3.rs-4225679/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4225679/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the field of industrial visual monitoring, PTZ cameras need to monitor the leakage of different easy leakage points. The backgrounds of the monitoring images change greatly and the characteristics of leakage targets are small, which will lead to large intra-class differences and small inter-class differences in the dataset samples. These problems affect the ability of the detection network to learn different class features of the leakage images and restrict the leakage detection performance. To solve the above problems, this paper proposes a pipeline leakage video detection method based on loss joint supervision in multiple scenes. Firstly, C3D network is used to simultaneously extract the spatial and temporal characteristics of the pipeline leakage video. In addition, the random forest classifier is used to avoid the tedious gradient calculation operation in the training process. Finally, by adding triple loss and center loss to jointly supervise model training, we measure the similarity within classes and the difference between classes to improve the decision-making ability of the leakage detection classifier. 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