Railroad cross-ties (sleepers) play an important role in the function of a railroad by transferring loads to the track ballast and sub-grade, by holding rails upright, and by helping to maintain proper gauge thus preventing derailments.

However, wooden (timber) and concrete tie systems degrade over time and thus require efficient and objective inspections in order to ensure their integrity. Wooden ties can develop increasingly large splits, become rotten and fail to properly support the track. Concrete ties can develop both longitudinal and transverse cracking, and chipping from derailments and tamping operations. As well, both wooden and concrete ties can become skewed (no longer perpendicular to the rails).

However, tie inspection can be a challenging task for traditional 2D machine vision systems as they are limited to tie condition assessment based on merely the appearance of the tie condition as opposed to 3D measurements of the length, width and depth of splits, cracks and chips. Additionally, the surface of ties can become discolored over time reducing the already limited condition information available from 2D systems even further.

Pavemetrics^® LRAIL™ simplifies the tie inspection task by using Artificial Intelligence to analyze both 2D and 3D data in order to more accurately detect and report tie count, position, material, surface cover, tie skew, wooden tie grade and concrete tie grade.

Additionally, through the LRAIL’s Change Detection algorithms, changes in tie grading and skew between repeat runs can be automatically detected and reported pointing track maintenance crews to the portions of the network that are most in need of attention.

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