Deep Learning and Laser-Based 3-D Pixel-Level Rail Surface Defect Detection Method-Reference-Cited by-同舟云学术

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Deep Learning and Laser-Based 3-D Pixel-Level Rail Surface Defect Detection Method

Published:2023 Issue: Volume:72 Page:1-12
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Ye Jiaqi¹^ORCID,Stewart Edward¹^ORCID,Chen Qianyu¹^ORCID,Roberts Clive¹^ORCID,Hajiyavand Amir M.²^ORCID,Lei Yaguo³^ORCID

Affiliation:

1. Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham, U.K

2. Department of Mechanical Engineering, University of Birmingham, Birmingham, U.K

3. Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, Xi’an, China

Funder

Shift2Rail Joint Undertaking through the European Union’s Horizon 2020 Research and Innovation Program

China Scholarship Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10129146.pdf?arnumber=10129146

Reference41 articles.

1. Convolutional networks for voting-based anomaly classification in metal surface inspection

2. Method for automatic railway track surface defect classification and evaluation using a laser‐based 3D model

3. Automated Visual Defect Detection for Flat Steel Surface: A Survey

4. Rail wear inspection based on computer-aided design model and point cloud data

5. You Only Look Once: Unified, Real-Time Object Detection

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2. Neural Ordinary Differential Equation Networks for Fintech Applications Using Internet of Things;IEEE Internet of Things Journal;2024-06-15

3. A Hierarchical Transfer-Generative Framework for Automating Multianalytical Tasks in Rail Surface Defect Inspection;IEEE Internet of Things Journal;2024-06-15

4. An Anomaly Detection Method for Railway Track Using Semisupervised Learning and Vision-Lidar Decision Fusion;IEEE Transactions on Instrumentation and Measurement;2024

5. The Design of a Non-Contact Inspection System Integrated With the Time of Flight-Based Flaw Detection (TOFFD) Criterion to Investigate the Structural Integrity of the Rail Track;IEEE Transactions on Instrumentation and Measurement;2024

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