BRAC University Institutional Repository

Automatic detection of defective rail anchors

Show simple item record

dc.contributor.author Khan, Rubayat Ahmed
dc.contributor.author Islam, Samiul
dc.contributor.author Biswas, Rubel
dc.date.accessioned 2017-01-04T06:14:07Z
dc.date.available 2017-01-04T06:14:07Z
dc.date.issued 2014-11
dc.identifier.citation Khan, R. A., Islam, S., & Biswas, R. (2014). Automatic detection of defective rail anchors. Paper presented at the 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014, 1583-1588. doi:10.1109/ITSC.2014.6957919 en_US
dc.identifier.isbn 978-147996078-1
dc.identifier.uri http://hdl.handle.net/10361/7513
dc.description This conference paper was presented in 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014; Qingdao; China; 8 October 2014 through 11 October 2014 [© 2014 IEEE] The conference paper's definite version is available at: http://dx.doi.org/10.1109/ITSC.2014.6957919 en_US
dc.description.abstract Rail line anchors/fasteners are the metallic components that attach each line with the sleepers. These are essential rail components as absence of these often result in derailments. Therefore in order to prevent dangerous situations and ensuring safety rail lines are periodically inspected. Rail inspection in many countries especially in third world countries, like Bangladesh, is performed manually by a trained human operator who periodically walks along the track searching for visual anomalies. This manual inspection is lengthy, laborious and subjective. This paper presents a machine vision-based technique to automatically detect the presence of rail line anchors/fasteners using Shi - Tomasi and Harris - Stephen feature detection algorithms. This approach has confirmed to successfully detect scenarios with both grounded and missing anchors invoked in the experiment, with an accuracy of 83.55%, thus proving its robustness. en_US
dc.language.iso en en_US
dc.relation.uri http://ieeexplore.ieee.org/document/6957919/
dc.subject Computer vision en_US
dc.subject Inspection en_US
dc.subject Intelligent systems en_US
dc.subject Manual inspection en_US
dc.title Automatic detection of defective rail anchors en_US
dc.type Conference paper en_US
dc.description.version Published
dc.contributor.department Department of Computer Science and Engineering, BRAC University
dc.identifier.doi http://dx.doi.org/10.1109/ITSC.2014.6957919


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Policy Guidelines

Search BRACU Repository


Advanced Search

Browse

My Account

Statistics