A defect classification methodology for sewer image sets with convolutional neural networks-Reference-Cited by-同舟云学术

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A defect classification methodology for sewer image sets with convolutional neural networks

Published:2019-08 Issue: Volume:104 Page:281-298
ISSN:0926-5805
Container-title:Automation in Construction
language:en
Short-container-title:Automation in Construction

Author:

Meijer Dirk,Scholten Lisa^ORCID,Clemens Francois,Knobbe Arno

Funder

Technology Innovation for Sewer Condition Assessment

Engineering Sciences of the Netherlands Organisation for Scientific Research

RIONED Foundation

STOWA

Knowledge Program Urban Drainage

Publisher

Elsevier BV

Subject

Building and Construction,Civil and Structural Engineering,Control and Systems Engineering

Reference27 articles.

1. HAZard and OPerability (HAZOP) analysis for identification of information requirements for sewer asset management;Stanić;Struct. Infrastruct. Eng.,2014

2. The consistency of visual sewer inspection data;Dirksen;Struct. Infrastruct. Eng.,2013

3. Challenging issues in modeling deterioration of combined sewers;Wirahadikusumah;J. Infrastruct. Syst.,2001

4. Computer Vision: Algorithms and Applications;Szeliski,2010

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