Autonomous Robotic System for Detecting Rail Anchor Failures Using Computer Vision
This project addresses the issue of rail anchor failures that pose significant risks to rail safety. The anchor failure can cause track misalignment, rail buckling, and derailments and adds to the maintenance costs. The proposed innovation is an autonomous robotic system designed to detect and locate rail anchor failures with precision and efficiency. By leveraging computer vision and machine learning, the system will identify problems such as slippage, damage, or displacement of rail anchors in real-time and provide precise location mapping for targeted maintenance interventions. The research approach will involve creating a high-resolution annotated image dataset and training a deployable artificial intelligence (AI) model to enable accurate, consistent, and scalable anchor inspections — capabilities that are not adequately addressed by existing systems. The work will be performed in two phases. The first phase will complete the design and development of the robotic platform equipped with high-resolution cameras and AI-based diagnostic tools. In the second phase, experimental validation of the system will be carried out on a railway track to ensure scalability, reliability, and effectiveness under real-world conditions. The system will automate the inspection process, eliminate human error, and enable proactive maintenance resulting in reduced downtime and enhanced rail safety. The scalable design and integration of advanced diagnostics will make the system suitable for adoption by the railroad industry on a wider scale and contribute to enhanced rail infrastructure reliability and operational efficiency.
Language
- English
Project
- Status: Active
- Funding: $99,921.00
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Contract Numbers:
IDEA-59
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Sponsor Organizations:
Federal Railroad Administration
1200 New Jersey Avenue, SE
Washington, DC United States 20590 -
Managing Organizations:
500 Fifth Street, NW
Washington, DC United States 20001 -
Project Managers:
Jawed, Inam
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Performing Organizations:
University of Texas Rio Grande Valley
, -
Principal Investigators:
Amjadian, Mohsen
- Start Date: 20260601
- Expected Completion Date: 20280531
- Actual Completion Date: 0
- USDOT Program: Rail Safety IDEA Program
Subject/Index Terms
- TRT Terms: Automation; Computer vision; Flaw detection; Image analysis; Inspection; Machine learning; Maintenance of way; Railroad tracks
- Subject Areas: Data and Information Technology; Maintenance and Preservation; Railroads;
Filing Info
- Accession Number: 02001762
- Record Type: Research project
- Source Agency: Transportation Research Board
- Contract Numbers: IDEA-59
- Files: TRB, RIP
- Created Date: Sep 4 2026 8:28AM