Conference Paper

Permanent URI for this collectionhttps://hdl.handle.net/10361/7504

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Automatic measurement of rail line expansion joint gaps
(© 2014 Institute of Electrical and Electronics Engineers Inc., 2015-03) Islam, Samiul; Khan, Rubayat Ahmed; Biswas, Rubel; Department of Computer Science and Engineering
Expansion joint gaps are the gaps which are deliberately left between the rail ends to allow for expansion of the rails in hot weather. Over gapping of these end to end gaps 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. Such manual inspection is lengthy, laborious and subjective. This paper presents a machine vision-based technique to automatically measure the length of rail line expansion joint gaps using morphological processing. This approach has confirmed to successfully detect scenarios of different condition with an accuracy of 89%, thus proving its robustness.
Automatic detection of defective rail anchors
(2014-11) Khan, Rubayat Ahmed; Islam, Samiul; Biswas, Rubel; Department of Computer Science and Engineering
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.
Clustering and detection of good and bad rail line anchors from images
(© 2015 Institute of Electrical and Electronics Engineers Inc., 2016-06) Islam, Samiul; Khan, Rubayat Ahmed; Department of Computer Science and Engineering
Absence of railway anchors/fasteners is a serious concern as it might lead to severe consequences such as train derailments. Hence regular inspection is an obligation to ensure safety. The third world countries choose the inspection process to be non-automatic where a trained operator moves along the rail line boarding a motor trolley checking for visual anomalies. In the previous research [1], an automatic system was proposed to overcome the cons of the running manual technique by using image processing. Two feature detection algorithms - Shi Tomasi and Harris Stephen - were used and an accuracy of 83.55% was achieved. This research presents an upgraded version of the previous work by introducing Neural Network. The addition of NN has not only speeded up the detection process but increased the accuracy significantly to approximately 93.86%.

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