Content area

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.

Details

1007133
Title
Deep Learning-Enhanced Jewelry Material Jadeite Jade Quality Assessment
Author
Meng, Liang 1 ; Effendi, Raja Ahmad Azmeer Raja Ahmad 1 ; Sun, Wei 2 ; Mo, Lili 3 ; Rahman, Ahmad Rizal Abdul 1 ; Hsu, Yu-lin; Barron, Deirdre

 Faculty of Design and Architecture, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia 
 Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia 
 Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia 
Publication title
JOM; New York
Volume
77
Issue
1
Pages
211-224
Publication year
2025
Publication date
Jan 2025
Section
TECHNICAL ARTICLE
Publisher
Springer Nature B.V.
Place of publication
New York
Country of publication
Netherlands
Publication subject
ISSN
10474838
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
ProQuest document ID
3159699230
Document URL
https://www.proquest.com/scholarly-journals/deep-learning-enhanced-jewelry-material-jadeite/docview/3159699230/se-2?accountid=208611
Copyright
Copyright Springer Nature B.V. Jan 2025
Last updated
2025-07-22
Database
ProQuest One Academic