Railway expansion joint gaps and hooks detection using morphological processing, corner points and blobs
This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2014.
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BRAC University
2014
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| Online dostop: | http://hdl.handle.net/10361/3742 |
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10361-3742 |
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10361-37422022-01-26T10:18:18Z Railway expansion joint gaps and hooks detection using morphological processing, corner points and blobs Islam, Samiul Biswas, Rubel Alam, Jahangir Department of Computer Science and Engineering, BRAC University Computer science and engineering This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2014. Cataloged from PDF version of thesis report. Includes bibliographical references (page 102 - 103). Rail inspection is an essential task in railway maintenance. It is periodically needed for preventing dangerous situations and ensuring safety in railways. In Bangladesh it has been seen many train accidents occur due to over gapping between rail lines and also due to missing of hooks which attach the tracks to the ground. At present, this task is operated 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 thesis presents a machine vision-based technique to automatically detect the presence of rail line hook and measure the gaps between each line to check whether the gap is safe or not. This inspection system uses real images acquired by a digital line scan camera installed under an automatic vehicle. Data are processed according to a combination of image processing and pattern recognition methods to achieve high performance automated detection. The scope of this project is strictly limited to the development of a machine vision based program capable of detecting the presence of parts of interest in rail tracks, from given rail track images. Rubayat Ahmed Khan B. Computer Science and Engineering 2014-09-29T04:50:43Z 2014-09-29T04:50:43Z 2014 2014-04 Thesis ID 12301053 ID 11301026 http://hdl.handle.net/10361/3742 en BRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 103 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Computer science and engineering |
| spellingShingle |
Computer science and engineering Islam, Samiul Railway expansion joint gaps and hooks detection using morphological processing, corner points and blobs |
| description |
This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2014. |
| author2 |
Biswas, Rubel |
| author_facet |
Biswas, Rubel Islam, Samiul |
| format |
Thesis |
| author |
Islam, Samiul |
| author_sort |
Islam, Samiul |
| title |
Railway expansion joint gaps and hooks detection using morphological processing, corner points and blobs |
| title_short |
Railway expansion joint gaps and hooks detection using morphological processing, corner points and blobs |
| title_full |
Railway expansion joint gaps and hooks detection using morphological processing, corner points and blobs |
| title_fullStr |
Railway expansion joint gaps and hooks detection using morphological processing, corner points and blobs |
| title_full_unstemmed |
Railway expansion joint gaps and hooks detection using morphological processing, corner points and blobs |
| title_sort |
railway expansion joint gaps and hooks detection using morphological processing, corner points and blobs |
| publisher |
BRAC University |
| publishDate |
2014 |
| url |
http://hdl.handle.net/10361/3742 |
| work_keys_str_mv |
AT islamsamiul railwayexpansionjointgapsandhooksdetectionusingmorphologicalprocessingcornerpointsandblobs |
| _version_ |
1814308781180321792 |