Experimental Investigations of a Convolutional Neural Network Model for Detecting Railway Track Anomalies-Reference-Cited by-同舟云学术

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Experimental Investigations of a Convolutional Neural Network Model for Detecting Railway Track Anomalies

Published:2023-10-16 Issue: Volume: Page:
ISSN:
Container-title:IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society
language:
Short-container-title:

Author:

Ji Albert¹,Thee Quek Yang²,Woo Wai Lok³,Wong Eugene⁴

Affiliation:

1. Newcastle University

2. Republic Polytechnic,Singapore

3. Northumbria University

4. Singapore Institute of Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10311571/10311610/10312404.pdf?arnumber=10312404

Reference27 articles.

1. Real time detection system for rail surface defects based on machine vision

2. A Real-Time Visual Inspection System for Railway Maintenance: Automatic Hexagonal-Headed Bolts Detection

3. A Machine Vision System for Automated Joint Bar Inspection From a Moving Rail Vehicle

4. Deep learning

5. Robust Fastener Detection for Autonomous Visual Railway Track Inspection

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. MDC-Net: Multimodal Detection and Captioning Network for Steel Surface Defects;Communications in Computer and Information Science;2024

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