TrackSafe: A comparative study of data-driven techniques for automated railway track fault detection using image datasets-Reference-Cited by-同舟云学术

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TrackSafe: A comparative study of data-driven techniques for automated railway track fault detection using image datasets

Published:2023-10 Issue: Volume:125 Page:106622
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Minguell Marta Garcia,Pandit Ravi^ORCID

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference46 articles.

1. RAILS deliverable d 1.1: Definition of a reference taxonomy of AI in railways;Anon,2020

2. Railway Safety Statistics in the EU - Statistics Explained;Anon,2021

3. Kaggle railway track datasets;Anon,2022

4. Intelligent machine vision based railway infrastructure inspection and monitoring using UAV;Banić;Facta Univ. Ser.: Mech. Eng.,2019

5. Automated vehicle for railway track fault detection;Bhushan;IOP Conf. Ser.: Mater. Sci. Eng.,2017

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2. Uncertainty inspired domain adaptation network for rail surface defect segmentation;Engineering Applications of Artificial Intelligence;2024-09

3. RailTrack-DaViT: A Vision Transformer-Based Approach for Automated Railway Track Defect Detection;Journal of Imaging;2024-08-07

4. Classification of battery laser welding defects via enhanced image preprocessing methods and explainable artificial intelligence-based verification;Engineering Applications of Artificial Intelligence;2024-07

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