Transfer Learning-Based Center-of-Mass Positioning Methods for Cultural Relics

Lin He, Quan Wei, Mengting Gong, Xiaofei Yang,Jianming Wei

IEEE ACCESS(2024)

引用 0|浏览1
暂无评分
摘要
In order to address the problem of incomplete data set of real seismic impact of cultural relics in collections, we propose a small sample set oriented center-of-mass positioning method to solve the center-of-mass attribute of cultural relics ontology. First, the method combines the cultural relics image data to construct a deep feature fusion cultural relics ontology recognition model, which extracts and learns the cultural relics image features through multi-layer feature extraction, global information perception and ontology fusion recognition, so as to realize the efficient and accurate recognition of the cultural relic's ontology. Then, the parameter synchronous migration learning method is designed to train and initialize the model, and the data migration strategy of semi-supervised learning is used to retrain the model using the public dataset and the cultural relic image data, to achieve the fine-tuning of the model parameters to improve the accuracy of the cultural relic ontology recognition. Finally, the center-of-mass positioning method is designed to integrate the attention mechanism with the quality of relics and other ontological attributes, and the segmentation of the relic's ontology region is achieved by rectangular stereo fitting and the calculation of the center-of-mass points. At the same time, the fusion self-attention mechanism is used to adjust the regional center-of-mass weights to achieve weighted positioning of the center-of-mass position of the cultural relics. The experimental results show that the present method achieves the best Dice, Acc and MIoU metrics in comparison with various classical models, with improvements of 8.3%, 5.1% and 3.4% respectively. The overall center-of-mass offset of cultural relics is less than 2%, which can achieve accurate identification and center-of-mass positioning of the cultural relics body, fully improve the seismic impact dataset of cultural relics, and enhance the ability of preventive protection of cultural relics against earthquakes.
更多
查看译文
关键词
Transfer learning,center-of-mass localization,attentional mechanisms,image recognition
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要