Construction of the PSO-LSSVM prediction model for sleeve pattern dimensions based on garment flat recognition
International Journal of Clothing Science and Technology
ISSN: 0955-6222
Article publication date: 22 September 2022
Issue publication date: 7 March 2023
Abstract
Purpose
The main purpose is to construct the mapping relationship between garment flat and pattern. Particle swarm optimization–least-squares support vector machine (PSO-LSSVM), the data-driven model, is proposed for predicting the pattern design dimensions based on small sample sizes by digitizing the experience of the patternmakers.
Design/methodology/approach
For this purpose, the sleeve components were automatically localized and segmented from the garment flat by the Mask R-CNN. The sleeve flat measurements were extracted by the Douglas–Peucker algorithm. Then, the PSO algorithm was used to optimize the LSSVM parameters. PSO-LSSVM was trained by utilizing the experience of patternmakers.
Findings
The experimental results demonstrated that the PSO-LSSVM model can effectively improve the generation ability and prediction accuracy in pattern design dimensions, even with small sample sizes. The mean square error could reach 1.057 ± 0.06. The fluctuation range of absolute error was smaller than the others such as pure LSSVM, backpropagation and radial basis function prediction models.
Originality/value
By constructing the mapping relationship between sleeve flat and pattern, the problems of the garment flat objective recognition and pattern design dimensions accurate prediction were solved. Meanwhile, the proposed method overcomes the problem that the parameters are determined by PSO rather than empirically. This framework could be extended to other garment components.
Keywords
Acknowledgements
This study is financially supported by the National Natural Science Foundation of China (No. 11671009), the Key Laboratory of Silk Culture Inheriting and Products Design Digital Technology, Ministry of Culture and Tourism (No. 2020WLB09) and the National Undergraduate Innovation and Entrepreneurship Training Program (No. 202210338032).
Citation
Li, T., Lyu, Y., Guo, Z., Du, L. and Zou, F. (2023), "Construction of the PSO-LSSVM prediction model for sleeve pattern dimensions based on garment flat recognition", International Journal of Clothing Science and Technology, Vol. 35 No. 1, pp. 67-87. https://doi.org/10.1108/IJCST-06-2021-0076
Publisher
:Emerald Publishing Limited
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