基于RSS-YOLOv5s模型的现代汉服风格检测方法

A Detection Method of Modern Hanfu Style Based on RSS-YOLOv5s Model

汉服做为一种穿着时尚,深受年轻人的喜爱,但现代汉服的风格信息却难以被许多汉服爱好者准确辨识。在YOLOv5s模型的基础上,插入Repvgg模块的同时,引入SE注意力机制来提高模型的网络特征提取能力;使用SIoU_Loss优化损失函数提升边界框定位精度,从而达到实时检测汉服风格的目的。结果表明:该算法明显改善多项评价指标,整体精确率达到92.4%,召回率达到91.6%,平均精度均值达到91.8%,单张图像推理时间仅需15.0 ms。该方法能够快速准确地辨识汉服风格,帮助人们了解现代汉服的风格特征,为中华优秀传统文化的传承发展提供技术支持。

Hanfu has become a kind of wearing fashion for young people,but its modern style information is difficult to be accurately identified by many Hanfu fans.Therefore,based on the YOLOv5s model,this paper improved the network feature extraction capability by inserting the Repvgg module and introducing the SE attention mechanism at the same time,and optimized the loss function using SIoU_Loss to improve the bounding box positioning accuracy,so as to realize the real-time detection of Hanfu style.The experimental results show that the algorithm achieves significant improvement in a number of evaluation indexes,with an overall precision rate of 92.4%,a recall rate of 91.6%,an average precision mean of 91.8%,and a single-image inference time of only 15.0 ms.The method can recognize the style of Hanfu quickly and accurately,thus helping people to understand the stylistic characteristics of modern Hanfu and providing technical support for the inheritance and development of Chinese excellent traditional culture.