Machine Vision for Analysing the Position of Fastening Nails on Wooden Railway Sleepers

Siril Yella, Jayalakshmi Baskar, and Mark Dougherty


Machine vision, Condition Monitoring, Clustering, Unsupervised learning


Wooden railway sleeper inspections in Sweden and to a large extent elsewhere are carried out manually by a human operator; visual inspection being the most common approach. Manually inspecting railway sleepers is slow and time consuming. Machine vision algorithms investigating surface cracks on the sleeper and sinking of the metal plate have been studied for the purpose of automating the task. In this particular article, information concerning how far the fastening nail has lifted out of position is investigated with an aim of using such information while assessing the condition of the sleeper. Laser beams incident on the sleeper have been used to highlight the geometrical form of the sleeper/plate/nail complex. Digital images of the nail were acquired mimic human visual capabilities. Appropriate image analysis techniques were applied to further process the images and necessary features were extracted. Results of unsupervised learning, achieved in the current work indicate that expectation maximization algorithm produced reliable results.

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