摘要
针对在服装图像语义分割中存在由服装颜色、纹理、背景以及多目标遮挡导致的边缘分割粗糙和分割精度低等问题,文中基于Deeplabv3+框架,提出了一种图像语义分割算法(FFDNet)。首先,模型的骨干网络采用ResNet101网络,并添加通道空间注意力模块(Feature-Enhanced Attention Module,FEAM),通过对特征图加权来挖掘并增强特征信息,提高网络表达能力。其次引入特征对齐模块(Feature Align Module,FAM)作为一种新的上采样方式,解决不同尺度特征融合之间特征未对齐导致分割错误且效率低的问题,以此提高对服装图像分割的准确性和鲁棒性。最后,FFDNet在Deepfashion2和PASCAL VOC 2012数据集上的平均交并比分别达到55.2%和79.4%;在参数量方面,该模型相比原模型在Deepfashion2上仅增加了0.61 MB。与其他现有经典模型对比,其分割性能更优,能有效捕获图像局部细节信息,减少像素分类错误。
Aiming at the problems of rough edge segmentation and low segmentation accuracy caused by color,texture,background and multi-object occlusion in clothing image segmentation,an image semantic segmentation method(FFDNet)based on Deeplabv3+with attention mechanism is proposed.Firstly,the backbone network of the model uses the ResNet101 network.The feature-enhanced attention module(FEAM)is added at the end of it.The feature map is weighted from the two dimensions of channel and spatial to mine and enhance the feature information and optimize the segmentation edge to improve network clarity.Secondly,a feature align module(FAM)is introduced as a novel upsampling method to address the problem of segmentation errors and low efficiency caused by misalignment between features during the fusion of different scale features,so as to to improve the accuracy and robustness of clothing image segmentation.Finally,the mean intersection over union of the proposed method reaches 55.2%and 79.4%on Deepfashion2 and PASCAL VOC2012,respectively.In terms of parameter size,the model only increases by 0.61MB compared to the original model on Deepfashion2.The segmentation performance of the FFDNet is superior to the existing state-of-the-art network models,which can effectively capture image local detail information and reduce pixel classification errors.
作者
肖雅慧
张自力
胡新荣
彭涛
张俊
XIAO Yahui;ZHANG Zili;HU Xinrong;PENG Tao;ZHANG Jun(School of Computer Science and Artificial Intelligence,Wuhan Textile University,Wuhan 430200,China;Engineering Research Center of Hubei Province for Clothing Information,Wuhan 430200,China;School of Computer Science and Engineering,Wuhan Institute of Technology,Wuhan 430205,China)
出处
《计算机科学》
CSCD
北大核心
2024年第S01期581-587,共7页
Computer Science
基金
湖北省教育厅科学技术研究计划项目(B2017066)。
