An Investigation of Railway Fastener Detection Using Image Processing and Augmented Deep Learning

Praneeth Chandran, Johnny Asber, Florian Thiery, Johan Odelius and Matti Rantatalo
Additional contact information
Praneeth Chandran: Division of Operation and Maintenance Engineering, Lulea University of Technology, 97187 Lulea, Sweden
Johnny Asber: Division of Operation and Maintenance Engineering, Lulea University of Technology, 97187 Lulea, Sweden
Florian Thiery: Division of Operation and Maintenance Engineering, Lulea University of Technology, 97187 Lulea, Sweden
Johan Odelius: Division of Operation and Maintenance Engineering, Lulea University of Technology, 97187 Lulea, Sweden
Matti Rantatalo: Division of Operation and Maintenance Engineering, Lulea University of Technology, 97187 Lulea, Sweden

Sustainability, 2021, vol. 13, issue 21, 1-15

Abstract: The rail fastening system forms an indispensable part of the rail tracks and needs to be periodically inspected to ensure safe, reliable and sustainable rail operations. Automated visual inspection has gained significant importance for fastener inspection in recent years. Position accuracy, robustness, and practical limitations due to the complex environment are some of the major concerns associated with this method. This study investigates the combined use of image processing and deep learning algorithms for detecting missing clamps within a rail fastening system. The images used for this study was acquired during field inspections carried out along the Borlänge-Avesta line in Sweden. The image processing techniques proposed in this study enabled the improvement of the fastener position and removal of redundant information from the fastener images. In addition, image augmentation was carried out to enhance the data set, ensure experimental reliability and replicate practical challenges associated with such visual inspection. Convolutional neural network and ResNet-50 algorithms are used for classification purposes, and both the algorithms achieved over 98% accuracy during training and validation and over 94% accuracy during the test stage. Both the algorithms also maintained a good balance between the precision and recall scores during the test stage. CNN and ResNet-50 algorithms were also tested to analyse their performances when the clamp areas were covered. CNN was able to accurately predict the fastener state up to 70% of clamp area occlusion, and ResNet-50 was able to achieve accurate predictions up to 75% of clamp area occlusion.

Keywords: rail fastening system; clamps; image processing; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/21/12051/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/21/12051/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:21:p:12051-:d:669660

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().