Research on Computer Vision Teaching of Mongolian Silver Jewelry Making Techniques
DOI:
https://doi.org/10.56294/mr2025216Keywords:
Mongolian Silver Jewelry, Defect Detection, Computer vision, Making Technology, Interactive Interface, Scalable Transient Search-tuned Multi-Cascaded Convolutional Neural Network (STS-MCCNN)Abstract
Introduction：Jewelry making is an integral part of Mongolian craftsmanship, reflecting the country's rich cultural heritage and artistic traditions. However, traditional methods often lack mechanisms for real-time feedback, limiting learner’s ability to detect and correct production defects. To address this issue, this research introduces computer vision-based teaching framework that integrates Deep learning (DL) for analyzing handcrafted silver jewelry, enhancing learning outcomes and preserving traditional artistry.
Method：The research utilizes the open-source Mongolian Silver Jewelry Defect Dataset (MSJDD) from Kaggle, comprising 1,050 samples of handcrafted jewelry. A Scalable Transient Search-tuned Multi-Cascaded Convolutional Neural Network (STS-MCCNN) model was developed to classify jewelry pieces, identify surface defects, and evaluate craftsmanship quality. The dataset was preprocessed using Min-max scaling to reduce noise, while Principal Component Analysis (PCA) was applied to improve feature extraction. The AI-driven interface enables users to input jewelry characteristics, receive automatic defect analysis, and visualize analytical heat maps highlighting critical defect zones, supported by an adaptive feedback mechanism for skill refinement.
Results：Experimental evaluations revealed that the proposed STS-MCCNN model achieved 98.5% accuracy, 98.36% recall, and 98.25% F1-score, supporting its high reliability in defect detection and craftsmanship evaluation. Moreover, the integration of real-time feedback significantly improved learner engagement and precision in jewelry-making techniques.
Conclusions：This research demonstrates how combining traditional artistry with AI technologies can preserve and modernize Mongolian jewelry-making practices. The proposed STS-MCCNN method enhances both learning and cultural continuity, offering a sustainable pathway for advancing artisan education and safeguarding intangible cultural heritage.
References
1. Thongnopkun P, Phlayrahan A, Madlee D, Roubroumlert W, Jamkratoke M. Effect of Thermal Treatment on Nano-and Micro-Copper Particles for Jewelry Making. Applied Sciences. 2022 Nov 25;12(23):12050. https://doi.org/10.3390/app122312050
2. Korium MS, Roozbahani H, Alizadeh M, Perepelkina S, Handroos H. Direct metal laser sintering of precious metals for jewelry applications: process parameter selection and microstructure analysis. IEEE Access. 2021 Sep 13;9: 126530-40. https://doi.org/10.1109/ACCESS.2021.3112479
3. Kenoyer JM, Cameron A, Bukhchuluun D, Amartuvshin C, Byambatseren B, Honeychurch W, Dussubieux L, Law R. Carnelian beads in Mongolia: new perspectives on technology and trade. Archaeological and Anthropological Sciences. 2022 Jan;14(1):6. https://doi.org/10.1007/s12520-021-01456-4
4. Zhang X, Fan Z, Shi Z, Pan L, Kwon S, Yang X, Liu Y. Tree characteristics and drought severity modulate the growth resilience of natural Mongolian pine to extreme drought episodes. Science of the Total Environment. 2022 Jul 15;830: 154742. https://doi.org/10.1016/j.scitotenv.2022.154742
5. Yu W. Research on innovation and development of Chinese traditional silver jewelry products under service design thinking. Arts Studies and Criticism. 2022;3(1):1-4. https://doi.org/10.32629/asc.v3i1.612
6. Suardana IW, Sumantra IM. The existence of pure and sacred silver craft creation in Gianyar Bali. Mudra Jurnal Seni Budaya. 2023 Jan 9;38(1):39-45. https://doi.org/10.31091/mudra.v38i1.2269
7. Wakiya T, Kamakura Y, Shibahara H, Ogasawara K, Saeki A, Nishikubo R, Inokuchi A, Yoshikawa H, Tanaka D. Machine‐learning‐assisted selective synthesis of a semiconductive silver thiolate coordination polymer with segregated paths for holes and electrons. Angewandte Chemie. 2021 Oct 18;133(43):23405-12. https://doi.org/10.1002/ange.202110629
8. Rowland Z, Blahova A, Peng GA. Silver as a value keeper and wealth distributor during an economic recession. Acta Montanistica Slovaca. 2021 Oct 1;26(4).https://doi.org/10.1134/S2635167621050037
9. Urbiztondo Castro MA, Rodrigo SG, Hamad S. Deep Learning-Based Energy Mapping of Chlorine Effects in an Epoxidation Reaction Catalyzed by a Silver–Copper Oxide Nanocatalyst. The Journal of Physical Chemistry C. 2023 Oct 31;127(44):21534-43. https://doi.org/10.1021/acs.jpcc.3c04308
10. Gür YE. Comparative analysis of deep learning models for silver price prediction: CNN, LSTM, GRU and hybrid approach. Akdeniz İİBF Dergisi. 2024;24(1):1-3. https://doi.org/10.25294/auiibfd.1404173
11. Magee MD. Generative Artificial Intelligence As A Tool For Jewelry Design. Gems & Gemology. 2024 Sep 1;60(3).https://doi.org/10.5741/GEMS.60.3.330
12. Khallaf EN, Ezz El-Din DM, El Shiwy RA. Reviving the Egyptian heritage of silver handicraft. Journal of the Faculty of Tourism and Hotels-University of Sadat City. 2022 Dec;6(2/1):147-70.
13. Tenuta L, Testa S, Antinarelli Freitas F, Cappellieri A. Sustainable Materials for Jewelry: Scenarios from a Design Perspective. Sustainability. 2024 Feb 4;16(3):1309. https://doi.org/10.3390/su16031309.
14. Purevdorj E. Comparative analysis of the concept of “child” in Mongolian and Korean expressions. Cogent Arts & Humanities. 2022 Dec 31;9(1):2128582. https://doi.org/10.1080/23311983.2022.2128582
15. Dhaundiyal D, Dangwal S. Ethnographic Insights into Bead Jewellery Traditions of the Women of the Van Gujjar Community of Uttarakhand. Chitrolekha J nal. 2024;8(1). https://doi.org/10.21659/cjad.81.v8n103
16. Wang Z, Li R. Automatic Optimization Algorithm of Jewelry Design based on Machine Vision. Computer-Aided Design & Applications. 2024; 21:85-102. https://doi.org/10.14733/cadaps.2024.S15.85-102
17. https://www.kaggle.com/datasets/ziya07/mongolian-silver-jewelry-defect-dataset-msjdd/data--
18. Li N. Metal jewelry craft design based on computer vision. Computational Intelligence and Neuroscience. 2022;2022(1):3843421. https://doi.org/10.1155/2022/3843421.
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