Efficient Railway Analysis Using Video

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.

Language

• English

Project

• Status: Completed
• Funding: $58656
• Contract Numbers:

  69A3551747132

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Project Managers:

  Teng, Hualiang

• Start Date: 20210801
• Expected Completion Date: 20230930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Computer vision; Costs; Inspection; Inspection equipment; Maintenance of way; Performance; Video cameras
• Subject Areas: Data and Information Technology; Railroads; Safety and Human Factors; Terminals and Facilities; Vehicles and Equipment;

Filing Info

• Accession Number: 01787402
• Record Type: Research project
• Source Agency: University Transportation Center on Improving Rail Transportation Infrastructure Sustainability and Durability
• Contract Numbers: 69A3551747132
• Files: UTC, RIP
• Created Date: Nov 5 2021 8:24PM