[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128364-en":3,"doc-seo-128364-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128364,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Integrating deep learning and machine learning for ceramic artifact classification and market value prediction - research","An intelligent framework is proposed for automated classification and valuation of ceramic artifacts by integrating deep learning and machine learning. An improved YOLOv11 model identifies key ceramic attributes including decorative patterns, shapes, and craftsmanship styles, reaching mAP@50 of 70.0% and recall of 91.0%. Visual attributes are then fed into a Random Forest classifier to predict price categories using multi-source auction data, achieving 99.52% test accuracy, with feature importance highlighting manufacturing techniques and shape as key predictors. The scalable workflow supports both experts and non-experts for digital heritage curation.","npj | heritage science Article  \n\n| \u003Cbr>[https://doi.org/10.1038/s40494-025-01886-6](https://doi.org/10.1038/s40494-025-01886-6) |\n| --- |\n| Integrating deep learning and machine learning for ceramic artifact classification and market value prediction\u003Cbr> Check for updates |\n\nYanfeng Hu1,3, Siqi Wu2,3 , Zhuoran Ma2 & Si Cheng1  \nThis study proposesan intelligent framework for the automated classification and valuation of ceramic artifacts, integrating deep learning and machine learning techniques. An improved YOLOv11 model was constructed to identify key ceramic attributes such as decorative patterns, shapes, and craftsmanship styles. The model achieved a mean Average Precision (mAP@50) of 70.0% and a recall of 91.0%, demonstrating strong capability in detecting complex visual features. Based on the extracted visual attributes, a Random Forest classifier was employed to predict price categories using multi-source auction data, achieving a test accuracy of 99.52% . Feature importance analysis further revealed manufacturing techniques and shape as key predictors of market value. The integrated framework effectively combines visual feature extraction and market-informed valuation, providing a scalable solution for intelligent ceramic appraisal and digital heritage curation. This approach supports both expert and non-expert applications, laying a foundation for future development of intelligent cultural heritage management systems.  \nCeramics are an important symbol of Chinese culture, embodying thousands of years ofartistic and technological heritage. They encompass a wide range of types, from pottery and painted ceramics to porcelain1. Swanson and Timothy2 (p. 45) highlight that ceramics serve as both artistic and utilitarian symbols. Beyond representing the esthetic aspirations ofdifferent historical periods, ceramics also play a crucial role in cultural preservation3.  \nIn addition to their cultural significance, the ceramics industry is a vital part of China’s manufacturing sector. Statistics indicate that the annual production of daily-use ceramics in China grew from 49.1 billion pieces in 2017 to 67.9 billion pieces in 2023, with an average annual growth rate of 5.55%4. According to Grand View Research5 (2021), the global ceramics market is projected to reach USD 347 billion by 2028, demonstrating immense economic potential. The compound annual growth rate (CAGR) between 2021and 2028 is expected tobe approximately 4.4%. These figures reveal that the ceramics market has vast potential for growth and development.  \nWith the rapid advancement of deep learning and computer vision technologies, image-based ceramic classification has become increasingly prevalent. These techniques demonstrate efficient and objective classification capabilities through methods such as feature extraction, image segmentation, and image enhancement6,7. Prior to deep learning dominance, researchers had already begun exploring how computer vision (CV) techniques could facilitate automated craftsmanship identification and address  \ntheinefficiencies oftraditional visual methods8–11. Traditional approaches to ceramic classification included empirical identification—highly reliant on expert knowledge and subjective visual judgment12, 13—as well as scientificidentification methods such as X-ray fluorescence, thermoluminescence dating, and spectral analysis, which, although precise, require complex instrumentation and domain expertise, limiting accessibility for nonprofessionals14. Recent studies have further enriched scientificidentification methods. For example, stereoscopic and polarizing microscopes have been used to analyze celadon from different dynasties15, and compositional analysis has helped determine kiln origins16. Additionally, diffuse reflectance spectral data have been employed to capture color characteristics across ceramic types17.  \nEarly computational methods applied hand-crafted feature descriptors such as Gradient Vector Flow (GVF) and Local Binary Patte","cbCaipCuatVJP6KW","https://ap.wps.com/l/cbCaipCuatVJP6KW","pdf",10575479,2,1,17,"English","en",105,"# Overview of the proposed framework\n## Ceramic attribute detection with YOLOv11\n## Price category prediction with Random Forest\n## Feature importance for market-value drivers","[{\"question\":\"What is the core goal of the study?\",\"answer\":\"To automatically classify ceramic artifacts and predict their market value using a combined deep learning and machine learning framework.\"},{\"question\":\"How does the model extract ceramic attributes?\",\"answer\":\"It uses an improved YOLOv11 model to identify visual attributes such as decorative patterns, shapes, and craftsmanship styles.\"},{\"question\":\"How are price categories predicted and how accurate is it?\",\"answer\":\"Extracted visual attributes are used by a Random Forest classifier trained on multi-source auction data, achieving 99.52% test accuracy.\"}]","Integrating deep learning and machine learning for ceramic artifact classification and market value prediction - 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