Automatic Classification and Recognition of Qinghai Embroidery Images Based on the SE‐ResNet152V2 Model

ABSTRACT As an essential part of traditional Chinese handicrafts, Qinghai embroidery embodies rich cultural connotations and unique artistic value. However, with the development of modern society, traditional handicrafts face severe challenges in inheritance and protection. To effectively address these challenges and promote Qinghai embroidery's digital safety and inheritance, this study realizes the automatic classification and identification of Qinghai embroidery images based on the SE‐ResNet152V2 model. First, we constructed an image dataset containing five kinds of Qinghai embroidery patterns, including Tu nationality Pan embroidery, Huangzhong Dui embroidery, Hehuang embroidery, Mongolian embroidery, and Tibetan Guinan embroidery. The regions that contribute the most when the model judges the image categories are revealed by the GRAD‐CAM technique, and the data are preprocessed by the image enhancement technique to enhance the data diversity and improve the model's generalization ability. For the complexity and detailed features of Qinghai embroidery patterns, this paper introduces the squeeze‐and‐excitation (SE) attention module to enhance the model's ability to capture key features. By systematically comparing the effects of multiple optimizers and attention mechanisms combination models, the optimal combination of the Nadam optimizer and SE attention mechanism is finally selected. The experimental results show that the accuracy of the optimized SE‐ResNet152V2 model on the self‐built Qinghai embroidery image dataset is 91.73%, which is 11.43% higher than that of the original ResNet152V2 model. Further experiments show that the SE‐ResNet152V2 model is better than other popular neural network models such as MobileNetV1, EfficientNetB2, vision transformer (VIT), and swin transformer, regarding classification accuracy. It has proven its effectiveness and superiority in processing Qinghai embroidery pattern recognition tasks and provided strong technical support for digital protection and inheritance of traditional crafts.