@article{guo_deep_2026,
	title = {Deep learning-based recognition of {Miao} ethnic costumes via {YOLOv5s}: {A} step toward digital cultural preservation},
	issn = {1222-5347},
	shorttitle = {Deep learning-based recognition of {Miao} ethnic costumes via {YOLOv5s}},
	url = {https://revistaindustriatextila.ro/images/2026/2/012%20RUI%20GUO_INDUSTRIA%20TEXTILA%20no.2_2026.pdf},
	doi = {10.35530/IT.077.02.2025156},
	abstract = {Miao ethnic costumes, celebrated for rich diversity, intricate craftsmanship, and distinctive patterns, represent an
important aspect of China’s cultural heritage and the broader realm of intangible cultural heritage. In response to the
growing need for digital preservation, this study proposes a deep learning-based approach to recognise and document
Miao costumes effectively. While traditional costume recognition methods face challenges such as high computational
costs and limited analytical capacity, the YOLOv5s framework offers automatic feature extraction and improved
scalability. However, its standard form struggles to adequately focus on critical visual features, reducing recognition
performance accuracy. To overcome this, we introduce the YOLOv5s-SED model, which incorporates a Squeeze-and-
Excitation (SE) attention mechanism and Deformable convolution (DCNv2) into YOLOv5s to enhance feature
representation and improve the recognition of fine details. A dedicated dataset of 4,468 annotated images was compiled,
and the model was refined through hyperparameter tuning and comparative experiments. The results demonstrate
notable performance gains, with precision increasing from 97.1\% to 97.6\%, recall from 99.3\% to 99.8\%, and mean
Average Precision (mAP) from 70.7\% to 71.5\%. These outcomes highlight the model’s strong generalisation ability in
complex environments and its potential to support the digital preservation and promotion of Miao ethnic costumes},
	urldate = {2026-05-17},
	journal = {Industria Textila},
	author = {Guo, Rui and Chen, Ting and Hong, Yan and Zeng, Xianyi},
	month = may,
	year = {2026},
	pages = {306},
}