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Deep Learning-Enhanced Jewelry Material Jadeite Jade Quality Assessment

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Publicado en:JOM vol. 77, no. 1 (Jan 2025), p. 211
Autor principal: Meng, Liang
Otros Autores: Effendi, Raja Ahmad Azmeer Raja Ahmad, Sun, Wei, Mo, Lili, Rahman, Ahmad Rizal Abdul, Hsu, Yu-lin, Barron, Deirdre
Publicado:
Springer Nature B.V.
Materias:
United States > US
China
Deep learning
Culture
Artificial neural networks
Jewelry
Machine learning
Research & development > R&D
Quality assessment
Artificial intelligence
Computer vision
Jewelry industry
Neural networks
Classification
Image classification
Design
Computer aided design > CAD
Algorithms
Image quality
Designers
Screening
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:1047-4838
0022-2674
0148-6608
0098-4558
DOI:10.1007/s11837-024-06930-7
Fuente:Science Database