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Reference TypeConference Paper (Conference Proceedings)
Last Update2021: (UTC)
Metadata Last Update2021: (UTC) administrator
Citation KeyJerripothulaAnsaNijh:2021:ViSoTr
TitleA Vision-based Solution for Track Misalignment Detection
Access Date2022, Jan. 22
Number of Files1
Size1619 KiB
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Author1 Jerripothula, Koteswar Rao
2 Ansari, Sharik Ali
3 Nijhawan, Rahul
Affiliation1 Indraprastha Institute of Information Technology Delhi (IIIT-Delhi)
2 College of Engineering Roorkee (COER)
3 University of Petroleum and Energy Studies (UPES)
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado (Virtual), Brazil
DateOctober 18th to October 22nd, 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2021-10-08 22:36:54 :: -> administrator :: 2021
2021-11-22 10:28:40 :: administrator -> :: 2021
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
transfer learning
AbstractDerailment 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. > SDLA > SIBGRAPI 2021 > A Vision-based Solution...
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