R-UNet++:A Local Segmentation Network for the Classification of Oracle Bone Materials

The rejoining of oracle bone(OB)fragments with the same material is a significant topic in the field of oracle bone inscriptions research.Facing to the challenges of OB materials classification,a classification framework based on R-UNet++is proposed.R-UNet++draws on the dense convolution block link in UNet++,and on this basis introduces the improved strategies by attention module,bilinear upsampling method and residual unit,which effectively inhibits the noise response generated by multi-scale feature fusion and enhances the finegrained segmentation capability in final.In this classification framework,first,R-UNet++is able to accurately divide the difference information between classes.Then,ResNet50 is adopted to further extract features from images segmented by R-UNet++and completes the OB materials classification task.With the real OB dataset,segmentation and classification experimental results show that R-UNet++has strong ability to accomplish the exact segmentation task.Compared with other state-of-the-art classification networks,the classification accuracy has a higher improvement,which fully verifies the feasibility and efficiency of proposed classification framework.