A multilevel segmentation method of asymmetric semantics based on deep learning

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-09-15 DOI:10.1049/cps2.12075
Angxin Liu, Yongbiao Yang
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Abstract

An asymmetric semantic multi-level segmentation method based on depth learning is proposed in order to improve the precision and effect of semantic segmentation. A ‘content tree’ structure and an adjacency matrix are constructed to represent the parent-child relationship between each image sub region in a complete image. Through multiple combinations of spatial attention mechanism and channel attention mechanism, the similarity semantic features of the target object can be selectively aggregated, so as to enhance its feature expression and avoid the impact of significant objects. The asymmetric semantic segmentation model asymmetric pyramid feature convolutional network (APFCN) is constructed, and the path feature extraction and parameter adjustment are realised through APFCN. On the basis of APFCN network, a full convolution network is introduced for end-to-end image semantic segmentation. Combining the advantages of convolution network in extracting image features and the advantages of short-term and short-term memory network in solving long-term dependence, an end-to-end hybrid depth network is constructed for image semantic multi-level segmentation. The experimental results show that the mean intersection over Union value and mean pixel accuracy value are higher than that of the literature method, both of which are increased by more than 3%, and the segmentation effect is good.

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基于深度学习的非对称语义多级分割方法
为了提高语义分割的精度和效果,本文提出了一种基于深度学习的非对称语义多层次分割方法。通过构建 "内容树 "结构和邻接矩阵来表示完整图像中每个图像子区域之间的父子关系。通过空间关注机制和通道关注机制的多重组合,可以有选择地聚合目标对象的相似性语义特征,从而增强其特征表达,避免重要对象的影响。构建非对称语义分割模型非对称金字塔特征卷积网络(APFCN),并通过APFCN实现路径特征提取和参数调整。在 APFCN 网络的基础上,引入全卷积网络进行端到端的图像语义分割。结合卷积网络在提取图像特征方面的优势和短时与短时记忆网络在解决长期依赖性方面的优势,构建了端到端混合深度网络,用于图像语义多层次分割。实验结果表明,平均交集过联合值和平均像素精度值均高于文献方法,均提高了 3% 以上,分割效果良好。
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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
期刊最新文献
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