Analysis and Recognition of Traditional Patterns in Guangxi Zhuang Ethnic Clothing Based on an Improved YOLOv5 Model

Ethnic culture, developed and integrated over time, manifests uniquely in language, architecture, and clothing styles. However, commercialization has led to “modernized” versions of ethnic attire that cater to popular aesthetics, often at the expense of cultural preservation, hindering understanding of traditional Zhuang culture and posing challenges for image recognition. Although deep learning technologies are increasingly applied to study ethnic patterns, most research remains confined to databases and requires high computational power, limiting its use in remote areas. This study incorporates SimAM and SEAttention mechanisms into the YOLOv5 framework to enable lightweight deployment and enhance the recognition of traditional Zhuang attire. The improved model increases accuracy by 10%-15%, reduces parameters by 1,312, and is well-suited for lightweight device recognition.