摘要
针对小尺寸服装与遮挡服装图像分割准确率低的问题,提出一种基于改进多尺度特征学习策略与注意力机制的服装图像分割方法。以Mask R-CNN为基础框架,首先采用增强特征金字塔网络优化模型的特征学习过程,对多尺度服装特征进行统一监督,缩小不同层级之间的语义差距,引入残差特征增强模块减少高层特征损失,采用软感兴趣区域选择自适应地获取最优感兴趣区域特征;然后在分类预测分支引入通道注意力模块,在边界框回归与掩膜预测分支分别引入空间注意力模块,提取图像中需要重点关注的服装区域特征。结果表明,与其他方法相比,本文方法改善了小尺寸服装图像和遮挡服装图像分割中存在的漏检、漏分割现象,提取出的服装实例更精确,其平均精度均值比原模型提升了3.8%。
Aiming at the low accuracy of small size clothing and occlusion clothing image segmentation,a clothing image segmentation method based on improved multi-scale feature learning strategy and attention mechanism was proposed,using Mask R-CNN as the basic framework.Augmentation feature pyramid network was used to optimize the feature learning process of the model,the semantic gap between different levels was narrowed through unified supervision of multi-scale clothing features,the residual feature enhancement module was introduced to reduce the loss of high level features,and soft region of interest selection was used to adaptively obtain the optimal region of interest characteristics.Then,the channel attention module was introduced into the classification prediction branch,the spatial attention modules were introduced into the bounding box regression and mask prediction branches respectively,and the clothing area features that need to be focused on in the image were extracted.The experimental results show that the proposed method can improve the detection rate and the segmentation completeness in dealing with small size clothing and occlusion clothing images,and the extracted clothing instance is more accurate comparing with the other methods,and the mean average precision is 3.8 points higher than that of the original model.
作者
顾梅花
刘杰
李立瑶
崔琳
GU Meihua;LIU Jie;LI Liyao;CUI Lin(School of Electronics and Information,Xi′an Polytechnic University,Xi′an,Shaanxi 710048,China)
出处
《纺织学报》
EI
CAS
CSCD
北大核心
2022年第11期163-171,共9页
Journal of Textile Research
基金
国家自然科学基金项目(61901347)。
关键词
服装图像分割
多尺度特征学习
一致监督策略
残差特征增强
软感兴趣区域选择
注意力机制
clothing image segmenttation
multi-scale feature learning
consistent supervision
residual feature augmentation
soft region of interest selection
attention mechanism
作者简介
第一作者:顾梅花(1980-),女,副教授,博士。主要研究方向为图像处理与分析。E-mail:gumh2001@163.com。
