{"paper_id":"4e66ee18-24af-4113-93f6-2abceef4d40d","body_text":"Blasting Ore Size Detection Based on Efficient Dehazing Network and Multi-Dimensional Feature Fusion | 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 Article Blasting Ore Size Detection Based on Efficient Dehazing Network and Multi-Dimensional Feature Fusion Pingfeng Li, Shoudong Xie, Wanzhong Zhang, Deming Chen, Sheng Peng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7982120/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Ore particle size distribution is an important metric to measure the ore blasting condition, but it is also an important parameter affecting the energy consumption of ore crushing equipment. Aiming at the challenges of the dense accumulation of ore, uneven size distribution, dust, and easy-to-lose targets due to motion, using computer vision methods, this paper proposes a blasting ore size detection method based on efficient dehazing network and multi-dimensional feature fusion based on YOLOv8. Firstly, this paper constructs an efficient defogging backbone network that combines feature attention and composite scalable backbone so that the model can efficiently extract the features of ore images and enhance the robustness of the model to dust interference in the ore crushing process. Secondly, this paper introduces a new feature fusion network that combines the convolution model and the Vmamba sequence model as well as cross-layer fusion of multi-scale features so that the model can effectively adapt to the dramatic scale change of blasting ore, capture fine ore and large-size ore, avoid ore omission, and improve the accuracy of particle size statistics. Finally, the multi-dimensional feature fusion ability of Dynamic Head was introduced to optimize the target detection head, and the feature fusion was further optimized so that the feature tensor obtained from the ore image was adapted to the detection and positioning task of ore, and the discrimination ability of the model for ore was improved. Experiments were conducted on a manually labeled jaw fracture ore dataset. Compared to the YOLOv8n algorithm, the average precision ( \\(\\:\\stackrel{-}{P}\\) ) for detecting eight size categories of ore increased by 7%. On datasets containing interference such as smoke, dust, and wet conditions, the mean average precision at the IoU threshold of 0.5 (mAP50) improved by 7.6%. For fine ores below D5 (72 mm), the detection precision ( \\(\\:\\stackrel{-}{P}\\) ) increased by 18.8%, while the recall rate ( \\(\\:\\stackrel{-}{R}\\) ) rose by 13.8%. On the total one-class dataset, the recall rate ( \\(\\:\\stackrel{-}{R}\\) ) and mAP50 reached 84% and 88.1%, respectively. Physical sciences/Engineering Physical sciences/Mathematics and computing particle size of ore deep learning object detection efficient network dust Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 Nov, 2025 Reviews received at journal 11 Nov, 2025 Reviews received at journal 11 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers invited by journal 05 Nov, 2025 Editor invited by journal 04 Nov, 2025 Editor assigned by journal 30 Oct, 2025 Submission checks completed at journal 30 Oct, 2025 First submitted to journal 29 Oct, 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. 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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-7982120\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":543949250,\"identity\":\"5f9b16b7-3b87-4c6a-bdee-8d04ede6cf6a\",\"order_by\":0,\"name\":\"Pingfeng Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"National Mine Safety Administration\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Pingfeng\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":543949251,\"identity\":\"e93d195d-e8b6-4ada-81d7-31dffb49f8bb\",\"order_by\":1,\"name\":\"Shoudong 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Dehazing Network and Multi-Dimensional Feature Fusion\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"particle size of ore, deep learning, object detection, efficient network, dust\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7982120/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7982120/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eOre particle size distribution is an important metric to measure the ore blasting condition, but it is also an important parameter affecting the energy consumption of ore crushing equipment. Aiming at the challenges of the dense accumulation of ore, uneven size distribution, dust, and easy-to-lose targets due to motion, using computer vision methods, this paper proposes a blasting ore size detection method based on efficient dehazing network and multi-dimensional feature fusion based on YOLOv8. Firstly, this paper constructs an efficient defogging backbone network that combines feature attention and composite scalable backbone so that the model can efficiently extract the features of ore images and enhance the robustness of the model to dust interference in the ore crushing process. Secondly, this paper introduces a new feature fusion network that combines the convolution model and the Vmamba sequence model as well as cross-layer fusion of multi-scale features so that the model can effectively adapt to the dramatic scale change of blasting ore, capture fine ore and large-size ore, avoid ore omission, and improve the accuracy of particle size statistics. Finally, the multi-dimensional feature fusion ability of Dynamic Head was introduced to optimize the target detection head, and the feature fusion was further optimized so that the feature tensor obtained from the ore image was adapted to the detection and positioning task of ore, and the discrimination ability of the model for ore was improved. Experiments were conducted on a manually labeled jaw fracture ore dataset. Compared to the YOLOv8n algorithm, the average precision (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\stackrel{-}{P}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e) for detecting eight size categories of ore increased by 7%. On datasets containing interference such as smoke, dust, and wet conditions, the mean average precision at the IoU threshold of 0.5 (mAP50) improved by 7.6%. For fine ores below D5 (72 mm), the detection precision (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\stackrel{-}{P}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e) increased by 18.8%, while the recall rate (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\stackrel{-}{R}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e) rose by 13.8%. On the total one-class dataset, the recall rate (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\stackrel{-}{R}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e) and mAP50 reached 84% and 88.1%, respectively.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Blasting Ore Size Detection Based on Efficient Dehazing Network and Multi-Dimensional Feature Fusion\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-14 12:48:23\",\"doi\":\"10.21203/rs.3.rs-7982120/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-11-12T12:56:22+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-11-11T08:20:03+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-11-11T06:40:45+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"126429584555131456877917319493914194961\",\"date\":\"2025-11-08T10:12:50+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"138246443889113731924611870189327360427\",\"date\":\"2025-11-05T11:38:32+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-11-05T07:03:32+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-11-04T08:05:34+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-10-30T06:31:16+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-10-30T06:30:11+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Scientific Reports\",\"date\":\"2025-10-29T16:37:23+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"89dd50f5-7836-4170-84db-15a0fffbfd24\",\"owner\":[],\"postedDate\":\"November 14th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":57872527,\"name\":\"Physical sciences/Engineering\"},{\"id\":57872528,\"name\":\"Physical sciences/Mathematics and computing\"}],\"tags\":[],\"updatedAt\":\"2026-03-02T16:01:51+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-7982120\",\"link\":\"https://doi.org/10.1038/s41598-026-39514-3\",\"journal\":{\"identity\":\"scientific-reports\",\"isVorOnly\":false,\"title\":\"Scientific Reports\"},\"publishedOn\":\"2026-02-28 15:58:39\",\"publishedOnDateReadable\":\"February 28th, 2026\"},\"versionCreatedAt\":\"2025-11-14 12:48:23\",\"video\":\"\",\"vorDoi\":\"10.1038/s41598-026-39514-3\",\"vorDoiUrl\":\"https://doi.org/10.1038/s41598-026-39514-3\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7982120\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7982120\",\"identity\":\"rs-7982120\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}