Real-Time Semantic Segmentation for Railway Anomalies Analysis

Details

• Creators:
  Stanik, Paul III ; Morris, Brendan
• Corporate Creators:
  University of Nevada, Las Vegas ; University of Nevada. University Transportation Center on Improving Rail Transportation Infrastructure Sustainability and Durability
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Computer vision
  • Costs
  • Inspection
  • Inspection equipment
  • Maintenance of way
  • Performance
  • Video cameras
• Publication/ Report Number:
  UNLV-8
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  University of Nevada. University Transportation Center on Improving Rail Transportation Infrastructure Sustainability and Durability
• Abstract:
  In the past few years, computer vision has made huge jumps due to deep learning which leverages increased computational power and access to data. The computer vision community has also embraced transparency to accelerate research progress by sharing open datasets and open source code. Access to large scale datasets and bench- mark challenges propelled and opened the field. The autonomous vehicle community is a prime example.
• Format:
  PDF
• Funding:
  69A3551747132
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/78334/dot_78334_DS1.pdf
• File Type:
  [PDF - 2.07 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:d730bdc137e50ffe55a72dc34ffed224055beea35a007e0f1709657532245ca645cb1db0985199cbd00d0d6900f629e7d0ac702a2656fca87967a264b9189a3d