基于MSR-OverLoCK的唐卡图像识别OA

As an important carrier of Tibetan traditional painting art,Thangka exhibits visual characteristics such as complex composition,rich details,and slight inter-class differences,posing significant challenges to automatic recognition.To address this issue,we propose an improved OverLoCK network model for refined recognition of Thangka images.Based on the global-local collaborative framework of OverLoCK,the model incorporates three key improvements.Firstly,a Multi-Scale Gated Attention(MSGA)module is introduced into the Base-Net,which captures multi-scale feature information through a multi-branch parallel convolution structure and achieves adaptive feature fusion combined with the SE attention mechanism,enhancing the network's ability to extract multi-scale texture details.Second-ly,the ConvFFN in the Base-Net is replaced with the MK-ConvGLU module,improving the model's perception of local details.Finally,an Aggregated Attention Feature Enhancement(AAFE)module is integrated into the Focus-Net to realize accurate localization and fea-ture enhancement of key regions.Comparative experiments on the Thangka dataset show that compared with the baseline OverLoCK model,MSR-OverLoCK achieves an accuracy increase from 93.23%to 96.85%and an F1 improvement from 92.40%to 96.11%,out-performing mainstream models such as ResNet-50,ConvNeXt-T,and Swin Transformer significantly.These results verify the effective-ness of the proposed improvement strategies,providing a feasible technical solution for the intelligent recognition of Thangka images and the digital protection of cultural heritage.