Recognition of coal and gangue based on motion blur image using calibration matching method in LTCC

Recognition of coal and gangue based on motion blur image using calibration matching method in LTCC | 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 Recognition of coal and gangue based on motion blur image using calibration matching method in LTCC Jinwang Zhang, Xiaohang Wan, Geng He, Lianghui Li, Nan Wang, Yiqi Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5400853/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 Gangue is a kind of primarily by-product during the coal mining process, which has become one of the most important bulk solid wastes that need to be treated urgently in China. Hence, location recognition and edge detection of coal gangue with high accuracy is vital to intelligent longwall top coal caving (LTCC) mining. Aiming at the random distribution of different coal gangue types in fully mechanized caving face under the visible lens, a new method of gangue mixed ratio recognition by marker matching method was proposed. The locations of the motion-blurred images of coal and gangue were carried out, and an automatic recognition system of coal and gangue was developed based on the fusion features of grayscale and texture. The experimental results show that when the speed is in the range of 0.2 ~ 1.0m/s, the recognition accuracy of the coal and gangue type with small gray difference is more than 92%, and the recognition accuracy for the coal and gangue types with large gray difference is more than 97%. Furthermore, based on the improved HSV color space model of RGB, the morphological segmentation of the coal and gangue blocks are effectively carried out by using the mean binarization method. Longwall top coal caving mining Coal gangue Recognition accuracy Quadratic discriminant analysis HSV color space 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. 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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-5400853","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375099795,"identity":"33611a88-ed90-4075-bd3a-ffb05c82fc5c","order_by":0,"name":"Jinwang Zhang","email":"","orcid":"","institution":"China University of Mining and Technology (Beijing)","correspondingAuthor":false,"prefix":"","firstName":"Jinwang","middleName":"","lastName":"Zhang","suffix":""},{"id":375099796,"identity":"bc439d3b-9912-4cc1-be27-007a4cb90431","order_by":1,"name":"Xiaohang Wan","email":"","orcid":"","institution":"China University of Mining and Technology 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space","lastPublishedDoi":"10.21203/rs.3.rs-5400853/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5400853/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGangue is a kind of primarily by-product during the coal mining process, which has become one of the most important bulk solid wastes that need to be treated urgently in China. Hence, location recognition and edge detection of coal gangue with high accuracy is vital to intelligent longwall top coal caving (LTCC) mining. Aiming at the random distribution of different coal gangue types in fully mechanized caving face under the visible lens, a new method of gangue mixed ratio recognition by marker matching method was proposed. The locations of the motion-blurred images of coal and gangue were carried out, and an automatic recognition system of coal and gangue was developed based on the fusion features of grayscale and texture. The experimental results show that when the speed is in the range of 0.2\u0026thinsp;~\u0026thinsp;1.0m/s, the recognition accuracy of the coal and gangue type with small gray difference is more than 92%, and the recognition accuracy for the coal and gangue types with large gray difference is more than 97%. Furthermore, based on the improved HSV color space model of RGB, the morphological segmentation of the coal and gangue blocks are effectively carried out by using the mean binarization method.\u003c/p\u003e","manuscriptTitle":"Recognition of coal and gangue based on motion blur image using calibration matching method in LTCC","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-22 18:17:11","doi":"10.21203/rs.3.rs-5400853/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":"3ec9a6ed-7bbd-461a-81bf-f3645b94398e","owner":[],"postedDate":"November 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-20T23:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-22 18:17:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5400853","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5400853","identity":"rs-5400853","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}