基于上下文提取与注意力融合的遮挡服装图像分割  

Occlusive clothing image segmentation based on context extraction and attention fusion

作　　者：顾梅花[1] 花玮 董晓晓 张晓丹[1] GU Meihua;HUA Wei;DONG Xiaoxiao;ZHANG Xiaodan(School of Electronics and Information,Xi′an Polytechnic University,Xi′an,Shaanxi 710048,China)

机构地区：[1]西安工程大学电子信息学院,陕西西安710048

出　　处：《纺织学报》2024年第5期155-164,共10页Journal of Textile Research

基　　金：国家自然科学基金项目(61901347);陕西省科技厅面上项目(2022JM-146,2024JC-YBMS-491)。

摘　　要：针对遮挡服装图像分割准确率低的问题,提出一种融合上下文提取与注意力机制的遮挡服装图像实例分割方法。以Mask R-CNN为基础网络,首先采用上下文提取模块优化ResNet的输出特征,通过融合不同速率的多路径特征从多个感受野中捕获图像的上下文信息,强化遮挡服装特征表示的识别及提取能力;然后引入通道注意力机制与空间注意力机制的残差连接,自适应地专注于捕捉遮挡服装图像的空间和通道维度上的语义相互依赖关系,降低上下文提取模块在处理特征图时因冗余的上下文关系扩大造成误定位与误识别的概率;最后,采用目标检测损失函数CIoU计算原理作为非极大值抑制的评判标准,关注预测框和真实框的重叠与非重叠区域,最大程度地选择遮挡服装的最优目标框,使预测框更加贴近真实框。结果表明,与其它方法相比,改进方法显著改善了不同遮挡程度服装图像的误分割现象,能提取出更精确的服装实例,其对遮挡服装图像的平均分割精度比原模型提升了4.4%。Objective Visual analysis of clothing attracts attention,while convenitional methods for clothing parsing fail to capture richer information about clothing details due to various factors including complex backgrounds and mutual occlusion of clothing.Therefore,a novel clothing image instance segmentation method is proposed to effectively extract and segment the multi-pose and mutually occluded target clothing in complex scenes for the subsequent processing of clothing analysis,retrieval,and other tasks to better meet targeted needs for personalized clothing design,retrieval,and matching.Method The output features of ResNet were optimized by using a context extraction module to enhance the recognition and extraction of feature representations of occlusive clothing.Then the attention mechanism of residual connectivity was introduced to adaptively focus on capturing the semantic inter-dependencies in the spatial and channel dimensions of occlusive clothing images.As the last step,CIoU computational principle was used as the criterion for non-maximal suppression,while focusing on the overlapping and non-overlapping regions of the predicted box and the real box to select the optimal target box that covers the occlusive clothing to the fullest extent.Results In qualitative comparison with Mask R-CNN as well as Mask Scoring R-CNN and YoLact methods,the proposed method showed stronger mask perception and inference ability,effectively decoupling the overlapping relationship between masked garment instances with more accurate segmentation visual effect.In addition,accuracy(A P)was used as an evaluation index for further quantitative analysis of the improved model,and the segmentation accuracy A m P under different IoU was 49.3%,which was 3.6%higher than the original model.Meanwhile,by comparing the segmentation accuracy of each improved model for different occlusion degrees,it was seen that the Mask R-CNN model had the lowest segmentation accuracy for various occlusion degrees,while with the optimization of CEM,AM and CIoU

关 键 词：图像分割 遮挡服装 上下文提取 注意力机制 CIoU计算原理 

分 类 号：TS941.2[轻工技术与工程—服装设计与工程] TP391.4[自动化与计算机技术—计算机应用技术]