Principal Investigator/Researcher

Dr. Brendan Morris, Paul Stanik (Graduate Student), University of Nevada Las Vegas

Project Description

Camera-based systems for rail maintenance have had a long history.  However, traditional rail inspection methods require trains that are run during down time, have sensitive sensing/imaging equipment with high costs, or may require low speeds for analysis.  There is a tremendous opportunity to develop more low-cost analysis and maintenance systems which leverage recent advances increased computational power and access to data through deep learning for computer vision. 

This research aims to provide a low-cost camera-based railway analysis system using a single forward-facing locomotive camera.  Rather than providing detailed rail, tie and fastening wear or fatigue measurements, the system is targeted general railroad track condition and health at higher-level through identification of localized anomalies (vegetation, poor drainage, and surface ballast fouling) to target for more in-depth examination.  Existing railway semantic segmentation datasets will be used to pre-train detection algorithms while a new rail anomaly dataset will be collected for fine-tuning the network model.  Evaluation of popular deep learning techniques on a variety of low-cost hardware options will characterize the cost-performance trade-off.

Implementation of Research Outcomes

On-going

Impacts/Benefits of Implementation

N/A