A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance-Reference-Cited by-同舟云学术

A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

Published:2022 Issue: Volume:10 Page:65376-65400
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Donato Lorenzo De¹^ORCID,Flammini Francesco²^ORCID,Marrone Stefano¹^ORCID,Mazzariello Claudio³^ORCID,Nardone Roberto⁴^ORCID,Sansone Carlo¹^ORCID,Vittorini Valeria¹

Affiliation:

1. Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy

2. Department of Computer Science and Media Technology, Linnaeus University, Växjö, Sweden

3. Digital and Data Driven Innovation Unit, Hitachi Rail STS, Naples, Italy

4. Department of Engineering, University of Naples “Parthenope”, Naples, Italy

Funder

Shift2Rail Joint Undertaking

RAILS (Roadmaps for Artificial Intelligence

European Union’s Horizon 2020 research and innovation programme and the Shift2Rail JU members other than the Union

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09795283.pdf?arnumber=9795283

Reference176 articles.

1. How to get pavement distress detection ready for deep learning? A systematic approach

2. Deep Learning for Generic Object Detection: A Survey

3. Automatic Road Crack Detection Using Random Structured Forests

4. Edge computing: A survey

5. A systematic review of convolutional neural network-based structural condition assessment techniques

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