On the enhancement of semi-supervised deep learning-based railway defect detection using pseudo-labels

Author:

Ozdemir RidvanORCID,Koc MehmetORCID

Publisher

Elsevier BV

Reference48 articles.

1. Defect classification of railway fasteners using image preprocessing and alightweight convolutional neural network;Aydin;Turkish Journal Electrical Engineering and Computer Sciences,2022

2. Deep learning for imbalance data classification using class expert generative adversarial network;Cenggoro;Procedia Computer Science,2018

3. Deep convolutional neural networks for detection of rail surface defects;Faghih-Roohi,2016

4. Automatic fastener classification and defect detection in vision-based railway inspection systems;Feng;IEEE Transactions on Instrumentation and Measurement,2013

5. A hierarchical extractor-based visual rail surface inspection system;Gan;IEEE Sensors Journal,2017

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1. Railway rutting defects detection based on improved RT-DETR;Journal of Real-Time Image Processing;2024-08

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