Deep Learning-Enhanced Jewelry Material Jadeite Jade Quality Assessment

IF 2.1 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
JOM Pub Date : 2024-10-30 DOI:10.1007/s11837-024-06930-7
Liang Meng, Raja Ahmad Azmeer Raja Ahmad Effendi, Wei Sun, Lili Mo, Ahmad Rizal Abdul Rahman, Yu-Lin Hsu, Deirdre Barron
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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.

深度学习增强的珠宝材料翡翠质量评估
翡翠以其独特的质地和文化意义而闻名,是玉石品种的缩影,体现了中国玉石文化的最新演变。本研究旨在建立一个精确筛选翡翠质量的人工智能模型,利用深度学习技术彻底改变翡翠的设计和检测。目的是为珠宝公司、设计师和客户提供一种公正的翡翠质量分级和评估方法。我们精心策划了一个翡翠图像数据库,应用了预处理技术,并利用卷积神经网络(CNN)进行特征提取。结果令人鼓舞,该模型实现了显著的性能指标:玉石图像分类任务的准确率约为84.75%,召回率约为84.94%,F1得分约为73.76%。这些结果表明了该模型在翡翠质量评价中的有效性。将计算机辅助技术纳入翡翠筛选,预示着人工智能与传统玉雕设计无缝融合的变革时代的到来,标志着行业格局的关键转变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JOM
JOM 工程技术-材料科学:综合
CiteScore
4.50
自引率
3.80%
发文量
540
审稿时长
2.8 months
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.
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