A Coal Flow Point Cloud Sampling and Segmentation Algorithm for Belt Conveyors Based on Improved Graph Laplacian Operator

A Coal Flow Point Cloud Sampling and Segmentation Algorithm for Belt Conveyors Based on Improved Graph Laplacian Operator | 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 Coal Flow Point Cloud Sampling and Segmentation Algorithm for Belt Conveyors Based on Improved Graph Laplacian Operator Zonglei Mou, Xiao Liu, Lei Han, Peng Xu, Kai Zhu, Huadong Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7737204/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Binocular vision-based point cloud reconstruction is pivotal for real-time detection of coal flow on belt conveyors. It plays a critical role in ensuring the safe and energy-efficient operation of such conveyors. However, during binocular vision detection, the coal flow point cloud exhibits highly complex morphology, and coal lump edges remain notoriously challenging to segment. These issues significantly degrade the accuracy and real-time performance of point cloud reconstruction. To address this issue, a coal flow point cloud sampling and segmentation method for belt conveyors based on an improved graph Laplacian operator is proposed. Specifically, the captured coal flow point cloud is processed using a locally adaptive sampling method integrated with an information entropy optimization function. Meanwhile, a Laplace-PointNN model is constructed to extract features from the coal flow point cloud and establish a feature library. These steps enable precise segmentation of point clouds at the edges of coal lumps, thereby facilitating the completion of coal flow point cloud reconstruction. Experimental results show that the proposed method has an average relative error of 3.48%, and an 84.5% increase in detection speed with an average running time of 0.58 seconds. The results demonstrate that the algorithm can realize real-time detection of coal flow on belt conveyors, ensuring their safe and energy-efficient operation. Belt conveyor Point cloud sampling Point cloud segmentation Laplacian operator Non-parametric network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 13 Oct, 2025 Reviewers invited by journal 11 Oct, 2025 Editor assigned by journal 30 Sep, 2025 Submission checks completed at journal 30 Sep, 2025 First submitted to journal 28 Sep, 2025 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-7737204","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533049351,"identity":"2784b261-fcba-4550-a45c-6501360ed16f","order_by":0,"name":"Zonglei Mou","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zonglei","middleName":"","lastName":"Mou","suffix":""},{"id":533049353,"identity":"a73f2011-7db5-45b3-88a2-3dc87484a46d","order_by":1,"name":"Xiao Liu","email":"","orcid":"","institution":"Shandong University of Science and 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It plays a critical role in ensuring the safe and energy-efficient operation of such conveyors. However, during binocular vision detection, the coal flow point cloud exhibits highly complex morphology, and coal lump edges remain notoriously challenging to segment. These issues significantly degrade the accuracy and real-time performance of point cloud reconstruction. To address this issue, a coal flow point cloud sampling and segmentation method for belt conveyors based on an improved graph Laplacian operator is proposed. Specifically, the captured coal flow point cloud is processed using a locally adaptive sampling method integrated with an information entropy optimization function. Meanwhile, a Laplace-PointNN model is constructed to extract features from the coal flow point cloud and establish a feature library. These steps enable precise segmentation of point clouds at the edges of coal lumps, thereby facilitating the completion of coal flow point cloud reconstruction. Experimental results show that the proposed method has an average relative error of 3.48%, and an 84.5% increase in detection speed with an average running time of 0.58 seconds. The results demonstrate that the algorithm can realize real-time detection of coal flow on belt conveyors, ensuring their safe and energy-efficient operation.\u003c/p\u003e","manuscriptTitle":"A Coal Flow Point Cloud Sampling and Segmentation Algorithm for Belt Conveyors Based on Improved Graph Laplacian Operator","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 10:31:25","doi":"10.21203/rs.3.rs-7737204/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"168814838859067457469731703112677309123","date":"2025-10-13T22:36:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-11T22:32:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-30T10:12:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-30T10:10:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Signal, Image and Video Processing","date":"2025-09-29T02:24:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c63d6d7d-104b-4475-aa67-d1e33e3ac63b","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T10:31:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 10:31:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7737204","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7737204","identity":"rs-7737204","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}