基于RSKP-UNet模型的苗族服饰图案分割研究

Research on image segmentation of Miao costume patterns based on the RSKP-UNet model

为保护苗族服饰文化的传承,以及增加对苗族文化的研究为目的,文章以苗族服饰图案分割为研究内容,提出了一种基于RSKP-UNet(Residual Selective-Kernel Parallel U-Net)模型的苗族服饰图案分割算法。算法在U-Net模型的编码器部分加入Residual模块以提升模型的特征提取能力,在解码器部分嵌入SKNet模块和ParNet模块以增强模型的特征表达能力。通过引入的Lovász-hinge损失函数有效地解决了苗族服饰图案存在的样本类别不均衡的问题,实验结果还表明Lovász-hinge损失函数在各项分割指标上均优于最常用的BCE损失函数。文章提出的RSKP-UNet分割模型在该损失函数下进行训练,并与4种经典的深度学习分割模型进行分割性能对比,RSKP-UNet模型在各项分割指标上好于其他模型,相比于基准模型U-Net,在Dice系数、IoU、精确率、召回率及准确度等指标上分别提升了6.98%、11.07%、2.89%、6.75%及3.92%,可为苗族服饰图案分割研究提供有效可行的办法。

The Miao nationality is the sixth largest ethnic group in China,which has a history of thousands of years.It has created a unique material culture and spiritual culture in its development process,and the Miao costume is a highly condensed collection of the material and spiritual culture of the Miao nationality.As one of the unique symbols of Miao culture,the Miao costume has profound cultural heritage and cultural connotations.The patterns of the Miao costume are particularly eye-catching as they not only symbolize the wisdom of the Miao people in thousands of years of production and life,but also symbolize the pursuit of the good spirit of the Miao people.However,under the impact of modern pop culture and foreign culture,these cultural symbols are gradually disappearing.In order to protect and inherit them,the Miao costume pattern segmentation has become the most important work.However,the Miao costume pattern segmentation is quite difficult.At present,there are few studies on the extraction,classification,identification and preservation of the features of Miao costume pattern segmentation.With the excellent segmentation performance of the U-Net model and the advantages of easy deployment,the paper improves the basic structure of the U-Net model and proposes a Miao costume pattern segmentation algorithm based on the RSKP-UNet(Residual Selective-Kernel Parallel U-Net) model.The algorithm adds Residual modules in the encoder part of the U-Net model to improve the feature extraction capability of the model,and embeds the SKNet modules and ParNet modules in the decoder part to enhance the feature expression capability of the model.The paper uses evaluation metrics to measure the segmentation performance of the model and compares it with four segmentation models based on deep learning.The paper not only combines the current research focus-deep learning and attention mechanism,but also introduces the Lovász-hinge loss function to effectively solve the problem of class imbalance in the Miao costume patterns.The RSKP-UNet model is better than other models in various segmentation indicators.Compared with the benchmark model U-Net,the Dice coefficient,IoU,precision,recall and accuracy are improved by 6.98%,11.07%,2.89%,6.75% and 3.92%.The segmentation algorithm proposed in this paper realizes the extraction of the element content of the Miao costume patterns through image segmentation of Miao costume patterns,which can be used to build the Miao costume pattern database in this way,thus helping designers,relevant researchers and organizations to provide research foundation,and completing the protection and inheritance of the Miao costume culture.The paper also provides some reference for the segmentation research of other minority costume patterns.