{"paper_id":"1c6c1c2e-c008-46e5-ad09-e3559426172b","body_text":"Research on Dynasty Identification of Yue Kiln Celadon Using Multi-Scale Feature Fusion and Deep Learning | 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 Research on Dynasty Identification of Yue Kiln Celadon Using Multi-Scale Feature Fusion and Deep Learning Changyong Zhu, Xue Bai, Chunxia Zhang, Jiumei Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7353938/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract Yue Kiln celadon, an early Chinese porcelain of significant archaeological value, provides crucial insights into ancient techniques, cultural exchange, and diffusion. However, traditional expert-based dynasty authentication faces limitations in efficiency and objectivity. To address this challenge, we developed a deep learning approach with multi-scale feature fusion for efficient dynasty identification and feature extraction of Yue Kiln celadon spanning the Tang, Five Dynasties, and Bei Song periods. A curated dataset of 3,000 high-resolution celadon images was constructed and expanded to 9,000 images through standardized preprocessing and data augmentation. We designed a novel multi-scale feature fusion framework (HDBN-LGA) based on convolutional neural networks (CNNs), integrating an attention mechanism to accentuate discriminative features including texture, glaze characteristics, and morphology. Transfer learning strategies were further employed to mitigate sample imbalance and enhance model generalization capability. Experimental results demonstrate exceptional classification performance, achieving an average accuracy of 95.7%—specifically reaching 96.8%, 94.7%, and 95.5% for the Tang, Five Dynasties, and Bei Song periods, respectively, significantly outperforming conventional methods. The model exhibited notable robustness, maintaining accuracy declines within 3% when processing noisy or blurred images. This research establishes an efficient, objective methodology for dynasty authentication and scholarly analysis of Yue Kiln celadon, offering substantial academic and practical value through deep learning-driven multi-scale feature fusion. Yue Kiln celadon Deep learning Multi-scale feature fusion Dynasty identification multi-scale feature fusion Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Nov, 2025 Reviews received at journal 22 Nov, 2025 Reviewers agreed at journal 21 Nov, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviews received at journal 17 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviews received at journal 25 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviews received at journal 03 Sep, 2025 Reviewers agreed at journal 29 Aug, 2025 Reviewers invited by journal 29 Aug, 2025 Editor invited by journal 17 Aug, 2025 Editor assigned by journal 15 Aug, 2025 Submission checks completed at journal 15 Aug, 2025 First submitted to journal 12 Aug, 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. 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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-7353938\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":509954651,\"identity\":\"65ed16be-b9e6-46cf-b62a-4d4ea7cd00d0\",\"order_by\":0,\"name\":\"Changyong Zhu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Ningbo University of Finance and Economics\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Changyong\",\"middleName\":\"\",\"lastName\":\"Zhu\",\"suffix\":\"\"},{\"id\":509954652,\"identity\":\"d4e13101-1477-4cc3-bc50-b768f2af7cac\",\"order_by\":1,\"name\":\"Xue Bai\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACCSA+AEQJ/PyNjQ8+GNjYEa9FcsbhZsMZBWnJRGkB6UowOJDeJs3z4RBjAyEtkjOSNx74ueNOnmTDwWZjG4MDzAzsh49uwKdFWiKt4GDvmWfF/MyNjY9zDO7wMfCkpd3Ap0VOIsfgAG/b4cSZIFtyDJ4xM0jwmBHUcvAvUMuGA4lt0hYGhxkbCGmRBmo5zAvTwkCMFsmeZwWHZdueFUvOONhs2GOQlsxGyC8Sx5M3f3zbdiePn7/94YMff2zs+NkPH8OrBQgMULlsBJRj0TIKRsEoGAWjAB0AAIeKV4TgtOPlAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Ningbo University of Finance and Economics\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Xue\",\"middleName\":\"\",\"lastName\":\"Bai\",\"suffix\":\"\"},{\"id\":509954653,\"identity\":\"fa32d3ba-be44-4dbf-9d09-d0f4a1454749\",\"order_by\":2,\"name\":\"Chunxia Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tianjin University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chunxia\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":509954655,\"identity\":\"799987b0-57f7-4c60-9574-367b2bc63b71\",\"order_by\":3,\"name\":\"Jiumei Shen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Nantong University of Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jiumei\",\"middleName\":\"\",\"lastName\":\"Shen\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-08-12 09:23:31\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7353938/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7353938/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":90658408,\"identity\":\"3e2240a3-471b-44b7-89dd-5cf744ab6354\",\"added_by\":\"auto\",\"created_at\":\"2025-09-05 10:39:20\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":731403,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7353938/v1_covered_9a4800ee-db41-415c-b073-0e41242c0f7d.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Research on Dynasty Identification of Yue Kiln Celadon Using Multi-Scale Feature Fusion and Deep Learning\",\"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\":\"discover-artificial-intelligence\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"diai\",\"sideBox\":\"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Discover Artificial Intelligence\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Discover Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Yue Kiln celadon, Deep learning, Multi-scale feature fusion, Dynasty identification, multi-scale feature fusion\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7353938/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7353938/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eYue Kiln celadon, an early Chinese porcelain of significant archaeological value, provides crucial insights into ancient techniques, cultural exchange, and diffusion. However, traditional expert-based dynasty authentication faces limitations in efficiency and objectivity. To address this challenge, we developed a deep learning approach with multi-scale feature fusion for efficient dynasty identification and feature extraction of Yue Kiln celadon spanning the Tang, Five Dynasties, and Bei Song periods. A curated dataset of 3,000 high-resolution celadon images was constructed and expanded to 9,000 images through standardized preprocessing and data augmentation. We designed a novel multi-scale feature fusion framework (HDBN-LGA) based on convolutional neural networks (CNNs), integrating an attention mechanism to accentuate discriminative features including texture, glaze characteristics, and morphology. Transfer learning strategies were further employed to mitigate sample imbalance and enhance model generalization capability. Experimental results demonstrate exceptional classification performance, achieving an average accuracy of 95.7%\\u0026mdash;specifically reaching 96.8%, 94.7%, and 95.5% for the Tang, Five Dynasties, and Bei Song periods, respectively, significantly outperforming conventional methods. The model exhibited notable robustness, maintaining accuracy declines within 3% when processing noisy or blurred images. This research establishes an efficient, objective methodology for dynasty authentication and scholarly analysis of Yue Kiln celadon, offering substantial academic and practical value through deep learning-driven multi-scale feature fusion.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Research on Dynasty Identification of Yue Kiln Celadon Using Multi-Scale Feature Fusion and Deep Learning\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-09-05 10:15:14\",\"doi\":\"10.21203/rs.3.rs-7353938/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-11-26T20:08:55+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-11-22T09:51:15+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"175976854436650296711351022056740860734\",\"date\":\"2025-11-22T04:25:48+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"236101121860863590904032083504969719455\",\"date\":\"2025-11-18T08:52:55+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-11-17T09:57:25+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"269336253477916403817894815182197945777\",\"date\":\"2025-11-17T09:49:24+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"97884849275119146770228715276195479976\",\"date\":\"2025-11-17T07:09:38+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"97180062026370366372659368291527314280\",\"date\":\"2025-11-16T18:27:54+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-09-25T12:05:01+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"3981738361567954853109518626912926419\",\"date\":\"2025-09-14T10:53:00+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-09-04T01:59:14+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"238900673262839647730737268045401070356\",\"date\":\"2025-08-29T13:05:56+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-08-29T12:56:01+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-08-17T18:06:55+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-08-15T04:54:25+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-08-15T04:53:44+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Discover Artificial Intelligence\",\"date\":\"2025-08-12T09:19:18+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"discover-artificial-intelligence\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"diai\",\"sideBox\":\"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Discover Artificial Intelligence\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Discover Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"7d02890d-3ce3-4f50-96e6-fde7f82b0b8e\",\"owner\":[],\"postedDate\":\"September 5th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-06T09:54:40+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-09-05 10:15:14\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7353938\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7353938\",\"identity\":\"rs-7353938\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}