Liang Meng, Raja Ahmad Azmeer Raja Ahmad Effendi, Wei Sun, Lili Mo, Ahmad Rizal Abdul Rahman, Yu-Lin Hsu, Deirdre Barron
{"title":"Deep Learning-Enhanced Jewelry Material Jadeite Jade Quality Assessment","authors":"Liang Meng, Raja Ahmad Azmeer Raja Ahmad Effendi, Wei Sun, Lili Mo, Ahmad Rizal Abdul Rahman, Yu-Lin Hsu, Deirdre Barron","doi":"10.1007/s11837-024-06930-7","DOIUrl":null,"url":null,"abstract":"<div><p>Jadeite jade, renowned for its unique texture and cultural significance, stands as the epitome of jade varieties, embodying the latest evolution of China's jade culture. This research endeavors to establish an AI model for precisely screening jadeite quality, employing deep learning techniques to revolutionize jadeite design and detection. The objective is to provide jewelry companies, designers, and customers with an unbiased means of grading and evaluating jadeite quality. We have meticulously curated a database of jadeite images, applied preprocessing techniques, and have harnessed convolutional neural networks (CNN) for feature extraction. The outcomes were promising, with the model achieving notable performance indicators: an accuracy rate of approximately 84.75%, a recall rate of about 84.94%, and an F1 score of roughly 73.76% in jade image classification tasks. These results underscore the model's effectiveness in the assessment of jadeite quality. Incorporating computer-aided technology into jadeite screening foreshadows a transformative era where artificial intelligence seamlessly integrates with traditional jade carving design, signifying a pivotal shift in the industry's landscape.</p></div>","PeriodicalId":605,"journal":{"name":"JOM","volume":"77 1","pages":"211 - 224"},"PeriodicalIF":2.1000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JOM","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1007/s11837-024-06930-7","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Jadeite jade, renowned for its unique texture and cultural significance, stands as the epitome of jade varieties, embodying the latest evolution of China's jade culture. This research endeavors to establish an AI model for precisely screening jadeite quality, employing deep learning techniques to revolutionize jadeite design and detection. The objective is to provide jewelry companies, designers, and customers with an unbiased means of grading and evaluating jadeite quality. We have meticulously curated a database of jadeite images, applied preprocessing techniques, and have harnessed convolutional neural networks (CNN) for feature extraction. The outcomes were promising, with the model achieving notable performance indicators: an accuracy rate of approximately 84.75%, a recall rate of about 84.94%, and an F1 score of roughly 73.76% in jade image classification tasks. These results underscore the model's effectiveness in the assessment of jadeite quality. Incorporating computer-aided technology into jadeite screening foreshadows a transformative era where artificial intelligence seamlessly integrates with traditional jade carving design, signifying a pivotal shift in the industry's landscape.
期刊介绍:
JOM is a technical journal devoted to exploring the many aspects of materials science and engineering. JOM reports scholarly work that explores the state-of-the-art processing, fabrication, design, and application of metals, ceramics, plastics, composites, and other materials. In pursuing this goal, JOM strives to balance the interests of the laboratory and the marketplace by reporting academic, industrial, and government-sponsored work from around the world.