面向舌图像分割的高分辨率网络设计

High-Resolution network design for tongue image segmentation

针对舌图像分割算法存在边缘信息损失严重、分割精度低等问题,提出一种面向舌图像分割的高分辨率网络.首先对输入图片进行处理,构建多尺度子网并行连接的结构;其次引入注意力机制构建特征提取模块,加强对全局信息的提取;然后通过多尺度特征融合结构,充分融合低分辨率语义信息和高分辨率特征信息;最后通过空间金字塔池化结构进一步提取边界信息.通过在自建数据集上进行评估,相比于原始HRNet网络,所提算法平均交并比(MIOU)和像素准确率(ACC)分别提高了2.6、0.7个百分点.实验结果表明:所提算法有效提高了分割精度,减少边缘信息的损失,充分满足舌诊仪的需求.

A high-resolution network was proposed to solve the problems of severe loss of edge information and low segmentation accuracy.Firstly,the input images were processed to construct a multi-scale subnetwork parallel connection structure.Secondly,the attention mechanism was introduced to construct the feature extraction module to enhance the extraction of global information.Then,the low-resolution semantic information and high-resolution feature information were fully fused by multi-scale features.Finally,the boundary information was further extracted by the spatial pyramid structure.Compared with the original HRNet network,the average intersection ratio(MIOU)and pixel accuracy(ACC)of the proposed algorithm are improved by 2.6 and 0.7 percentage points,respectively,after evaluation on the self-built dataset.Experimental results show that the algorithm can effectively improve the segmentation accuracy,reduce the loss of edge information,and fully meet the requirements of tongue diagnostic instrument.