Automatic detection of defective rail anchors
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
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| Sarrera elektronikoa: | http://hdl.handle.net/10361/7513 http://dx.doi.org/10.1109/ITSC.2014.6957919 |
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10361-75132018-07-25T10:16:35Z Automatic detection of defective rail anchors Khan, Rubayat Ahmed Islam, Samiul Biswas, Rubel Department of Computer Science and Engineering, BRAC University Computer vision Inspection Intelligent systems Manual inspection 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 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. Published 2017-01-04T06:14:07Z 2017-01-04T06:14:07Z 2014-11 Conference paper 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 978-147996078-1 http://hdl.handle.net/10361/7513 http://dx.doi.org/10.1109/ITSC.2014.6957919 en http://ieeexplore.ieee.org/document/6957919/ |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Computer vision Inspection Intelligent systems Manual inspection |
| spellingShingle |
Computer vision Inspection Intelligent systems Manual inspection Khan, Rubayat Ahmed Islam, Samiul Biswas, Rubel Automatic detection of defective rail anchors |
| 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 |
| author2 |
Department of Computer Science and Engineering, BRAC University |
| author_facet |
Department of Computer Science and Engineering, BRAC University Khan, Rubayat Ahmed Islam, Samiul Biswas, Rubel |
| format |
Conference paper |
| author |
Khan, Rubayat Ahmed Islam, Samiul Biswas, Rubel |
| author_sort |
Khan, Rubayat Ahmed |
| title |
Automatic detection of defective rail anchors |
| title_short |
Automatic detection of defective rail anchors |
| title_full |
Automatic detection of defective rail anchors |
| title_fullStr |
Automatic detection of defective rail anchors |
| title_full_unstemmed |
Automatic detection of defective rail anchors |
| title_sort |
automatic detection of defective rail anchors |
| publishDate |
2017 |
| url |
http://hdl.handle.net/10361/7513 http://dx.doi.org/10.1109/ITSC.2014.6957919 |
| work_keys_str_mv |
AT khanrubayatahmed automaticdetectionofdefectiverailanchors AT islamsamiul automaticdetectionofdefectiverailanchors AT biswasrubel automaticdetectionofdefectiverailanchors |
| _version_ |
1814307108469866496 |