A Hierarchical Transfer-Generative Framework for Automating Multianalytical Tasks in Rail Surface Defect Inspection-Reference-Cited by-同舟云学术

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A Hierarchical Transfer-Generative Framework for Automating Multianalytical Tasks in Rail Surface Defect Inspection

Published:2024-06-15 Issue:12 Volume:11 Page:21513-21526
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Wang Tiange¹^ORCID,Zhang Zijun²^ORCID,Tsui Kwok-Leung³^ORCID

Affiliation:

1. Institute of Rail Transit and the National Maglev Transportation Engineering Research and Development Center, Tongji University, Shanghai, China

2. School of Data Science, City University of Hong Kong, Kowloon Tong, SAR, Hong Kong

3. Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

Funder

Shenzhen-Hong Kong-Macau Science & Technology Category C Project

CityU Strategic Interdisciplinary Research

InnoHK Initiative, The Government of the HKSAR

Laboratory for AI-Powered Financial Technologies

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10551641/10463617.pdf?arnumber=10463617

Reference51 articles.

1. Railway track fastener defect detection based on image processing and deep learning techniques: A comparative study

2. Cascade Learning Embedded Vision Inspection of Rail Fastener by Using a Fault Detection IoT Vehicle

3. Structural health monitoring of railway tracks using IoT-based multi-robot system

4. Detection for Rail Surface Defects via Partitioned Edge Feature

5. Segmentation of casting defect regions for the extraction of microstructural properties

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