万林江;黄成泉;张博源;王琴;周丽华;

• 1:贵州民族大学数据科学与信息工程学院
• 2:贵州民族大学工程技术人才实践训练中心

摘要(Abstract)：

针对苗族服饰因缺少像素级标注的数据库、元素多元化、纹饰图案不规则等引起的目标区域特征提取难度大的问题，提出了一种融合了注意力机制的SegNet分割模型(SE-SegNet)。在改进的SegNet模型中融入通道注意力SE模块，关注更多的细节特征，旨在于加强对目标特征的提取，实现苗服饰图案的自动分割。实验结果表明，该模型在苗族服饰数据集中，像素准确率为92.69%,交并比值为85.27%,相似系数为92.05%。与其他模型相比，该模型分割结果更精细，在苗族服饰图案分割的效果得到显著提升。服饰图案分割效果的提升对苗族服饰文化的保护和发展具有重要意义。

关键词(KeyWords)： 苗族服饰;SegNet模型;SE-SegNet;SE模块;图案分割

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(62062024);; 贵州省省级科技计划项目(黔科合基础-ZK[2021]一般342)

作者(Authors): 万林江;黄成泉;张博源;王琴;周丽华;

DOI: 10.19333/j.mfkj.20220506707

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