计算机与现代化Issue(10)：32-36,43,6.DOI:10.3969/j.issn.1006-2475.2025.10.006
基于改进的DeepLabv3+模型的自然环境下舌象分割方法
Natural Environment Tongue Image Segmentation Method Based on Improved Labv3+Model
摘要
Abstract
Tongue image segmentation in natural environments poses great challenges due to factors such as lighting and back-ground interference.This paper proposes a DeepLabv3-MAC model based on improved DeepLabv3+algorithm for segmenting tongue images captured in natural settings.Firstly,the backbone network of the DeepLabv3+model is replaced with a Mobile-Netv2 network to reduce model complexity.Secondly,an asymmetric convolutional module is adopted to enhance the convolu-tional kernel skeleton of convolutional neural network,thereby improving the utilization of convolutional information.Lastly,the introduction of the CBAM attention mechanism not only focuses on the importance of parameters in space and channels,but also enhances the segmentation accuracy of the model.Experimental results demonstrate that,compared to classical tongue image seg-mentation algorithms,the proposed DeepLabv3-MAC model exhibits superior segmentation performance.Additionally,the model significantly reduces the number of parameters,enabling faster segmentation of tongue images in natural environments and facili-tating future deployment on mobile devices.关键词
舌象分割/DeepLabv3+/DeepLabv3-MAC/非对称卷积模块/CBAM注意力机制Key words
tongue image segmentation/DeepLabv3+/DeepLabv3-MAC/asymmetric convolution module/CBAM attention mechanism分类
计算机与自动化引用本文复制引用
刘嵘澂,辛国江,张杨,朱磊..基于改进的DeepLabv3+模型的自然环境下舌象分割方法[J].计算机与现代化,2025,(10):32-36,43,6.基金项目
国家级大学生创新训练项目(2022批次) （2022批次）
湖南省一流本科课程(2021-896) （2021-896）
湖南省教改课题(HNJG-2021-0584) （HNJG-2021-0584）
