@InProceedings{JerripothulaAnsaNijh:2021:ViSoTr,
author = "Jerripothula, Koteswar Rao and Ansari, Sharik Ali and Nijhawan,
Rahul",
affiliation = "{Indraprastha Institute of Information Technology Delhi
(IIIT-Delhi) } and {College of Engineering Roorkee (COER) } and
{University of Petroleum and Energy Studies (UPES)}",
title = "A Vision-based Solution for Track Misalignment Detection",
booktitle = "Proceedings...",
year = "2021",
editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and
Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario
and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos,
Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira,
Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir
A. and Fernandes, Leandro A. F. and Avila, Sandra",
organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "railway, transfer learning, VGG, Inception.",
abstract = "Derailment is one of the most frequent ways railway accidents
happen. Track defects such as buckling and hogging that cause
misalignment of tracks can easily lead to derailments. While
railway tracks get laterally misaligned due to buckling, vertical
misalignments can result from hogging. Such misalignments are
visibly recognizable, and we can even automate recognition using
data-driven models. This paper discusses how we build such
data-driven models. There are no public datasets available to
build such models; therefore, we introduce TMD (Track Misalignment
Detection) dataset. It consists of misaligned and normal track
images. The problem we try to solve here is essentially a binary
image classification problem, which we solve by exploring the
feature extraction approach to transfer learning (TL). In this
approach, we employ a pre-trained network to extract rich
features, which are then supplied with annotations to a learning
algorithm for building a candidate TL model. Several pre-trained
networks and learning algorithms exist, resulting in multiple
candidate TL models; therefore, it becomes essential to identify
effective ones. We propose an evaluation criterion to decide which
are effective ones using our proposed bias-variance analysis. Our
experiments demonstrate that the candidate TL models selected
based on our proposed evaluation criterion perform better than
other candidate TL models while testing.",
conference-location = "Gramado, RS, Brazil (virtual)",
conference-year = "18-22 Oct. 2021",
doi = "10.1109/SIBGRAPI54419.2021.00044",
url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00044",
language = "en",
ibi = "8JMKD3MGPEW34M/45CUKQ8",
url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CUKQ8",
targetfile = "SIBGRAPI_Railway (3).pdf",
urlaccessdate = "2024, May 05"
}