PipeTransUNet: CNN and Transformer fusion network for semantic segmentation and severity quantification of multiple sewer pipe defects-Reference-Cited by-同舟云学术

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PipeTransUNet: CNN and Transformer fusion network for semantic segmentation and severity quantification of multiple sewer pipe defects

Published:2024-07 Issue: Volume:159 Page:111673
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Li Mingze^ORCID,Li Mingchao,Ren Qiubing^ORCID,Li Heng^ORCID,Xiao Lei,Fang Xin

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

Elsevier BV

Reference58 articles.

1. Experimental-numerical study on the structural failure of concrete sewer pipes;Scheperboer;Tunn. Undergr. Sp. Tech.,2021

2. Sanitary sewer overflows and emergency room visits for gastrointestinal illness: Analysis of Massachusetts data, 2006-2007;Jagai;Environ. Health Persp.,2017

3. 3D pipe reconstruction employing video information from mobile robots;Reyes-Acosta;Appl. Soft Comput.,2019

4. Automated diagnosis of sewer pipe defects based on machine learning approaches;Yang;Expert Syst. Appl.,2008

5. Pipeline in-line inspection method, instrumentation and data management;Ma;Sensors,2021

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Improved U-Net Drainage Pipe Defects Detection Algorithm Based on CBMA Attention Mechanism;2024 9th International Conference on Electronic Technology and Information Science (ICETIS);2024-05-17

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