MPFANet: Semantic Segmentation Using Multiple Path Feature Aggregation

被引：0

作者：

Jeon, Wang-Su ^{[1
]}

Rhee, Sang-Yong ^{[2
]}

机构：

[1] Kyungnam Univ, Dept IT Convergence Engn, Chang Won, South Korea

[2] Kyungnam Univ, Dept Comp Engn, Chang Won, South Korea

来源：

INTERNATIONAL JOURNAL OF FUZZY LOGIC AND INTELLIGENT SYSTEMS | 2021年 / 21卷 / 04期

基金：

新加坡国家研究基金会;

关键词：

MPFA; Semantic segmentation; Feature aggregation; CNN; Inverted residual block; Local context;

D O I：

10.5391/IJFIS.2021.21.4.401

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Image segmentation is the process of simplifying the analysis of the meaning or the front to say the process of dividing the image into a set of multiple pixels. The multiple path feature aggregation (MPFA) method proposed in this paper aims to extract various information of an object, and uses conventional pyramid pooling or the extraction of various sized features. This information can be combined with different regional features to obtain the overall feature information. We split four paths to extract numerous local features, and the results showed that the mean intersection over union (mIOU) is 81.6% for the validation data from the PASCAL VOC 2012 dataset, and a better performance than the existing DeepLab model was demonstrated.

引用

页码：401 / 408

页数：8

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