MSSINet: Real-Time Segmentation Based on Multi-Scale Strip Integration-Reference-Cited by-同舟云学术

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MSSINet: Real-Time Segmentation Based on Multi-Scale Strip Integration

Published:2024 Issue: Volume:8 Page:241-251
ISSN:2469-7281
Container-title:IEEE Journal of Radio Frequency Identification
language:
Short-container-title:IEEE J. Radio Freq. Identif.

Author:

Wang Lin¹^ORCID,Zhu Fenghua²^ORCID,Zhang Hui³^ORCID,Xiong Gang²^ORCID,Huang Yunhu⁴^ORCID,Chen Dewang¹^ORCID

Affiliation:

1. School Transportation, Fujian University of Technology, Fuzhou, China

2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

3. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

4. College of Computer and Data Science, Minjiang University, Fuzhou, China

Funder

Guangdong Provincial Key Area Research and Development Program Project

Jiangxi Provincial Natural Science Foundation

China National Railway Group Company Ltd. Science and Technology Research and Development Program Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7433271/10416935/10500690.pdf?arnumber=10500690

Reference45 articles.

1. Fully convolutional networks for semantic segmentation

2. U-Net: Convolutional Networks for Biomedical Image Segmentation

3. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

4. Deep High-Resolution Representation Learning for Visual Recognition

5. Rethinking atrous convolution for semantic image segmentation;Chen;arXiv:1706.05587,2017

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