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dc.contributor.authorKhan, Rubayat Ahmed
dc.contributor.authorIslam, Samiul
dc.contributor.authorBiswas, Rubel
dc.identifier.citationKhan, 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.2014.6957919en_US
dc.descriptionThis 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:
dc.description.abstractRail 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.subjectComputer visionen_US
dc.subjectIntelligent systemsen_US
dc.subjectManual inspectionen_US
dc.titleAutomatic detection of defective rail anchorsen_US
dc.typeConference paperen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University

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