Multi-Sensory System for Railway Track Defect Detection
Railway transportation is essential for moving passengers and freight across the U.S., but accidents continue to pose serious safety and economic risks. In 2022 alone, there were about 950 rail-related fatalities and 6,400 injuries nationwide. While human error and reckless behavior are major contributors, defective track infrastructure is a significant and preventable cause of accidents. Railway tracks are complex systems consisting of steel rails, crossties, fasteners, and ballast, all subject to heavy loads, temperature fluctuations, and environmental impacts. These stresses lead to issues such as broken rails, cracked or spalled crossties, loose or missing fasteners, geometry defects, and cross-level variations. Extreme weather conditions can further cause rail buckling or fracture. Failures in these components can trigger derailments, collisions, hazardous material spills, and major service disruptions. Although manual inspections and specialized vehicles are used, many defects go undetected between inspection cycles. Traditional manual inspections, although reliable for identifying visible rail defects, are labor-intensive and limited in scalability. To improve efficiency, various nondestructive testing (NDT) technologies, such as infrared imaging, acoustic emission, ultrasonic, and electromagnetic techniques, have been used primarily for internal defects. As surface defects become more prevalent, various methods have also been developed for detecting surface-level flaws, which can be broadly categorized into three approaches: static monitoring where sensors at fixed locations provide localized coverage; inspection trolleys which integrate sensors generally in the laboratory setting; and onboard sensing systems which enable real-time detection ahead of moving trains but suffer from high cost with varying imaging quality under different weather and lighting conditions. The primary objective of this project is to develop a comprehensive but low-cost multi-sensory system for railway track defect detection. The system will integrate binocular stereovision cameras, Global Navigation Satellite System / Global Positioning System (GNSS/GPS), and IMU sensors. The scope of this project includes development of a multi-sensory system including controller and field data acquisition, development of real-time data fusion and detection algorithms, and recommendations for system deployment on railway tracks.
- Record URL:
Language
- English
Project
- Status: Active
- Funding: $74,797.00
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Contract Numbers:
69A3552348306 (CY3-OSU-07)
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Sponsor Organizations:
Southern Plains Transportation Center
University of Oklahoma
202 W Boyd St, Room 213A
Norman, OK United States 73019Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
University of Oklahoma, Norman
School of Civil Engineering and Environmental Science
202 West Boyd Street, Room 334
Norman, OK United States 73019 -
Project Managers:
Ghasemi, Hamid
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Performing Organizations:
Oklahoma State University, Stillwater
School of Civil & Environmental Engineering
Stillwater, OK United States 74078 -
Principal Investigators:
Li, Joshua
- Start Date: 20260101
- Expected Completion Date: 20270101
- Actual Completion Date: 0
- USDOT Program: UTC
Subject/Index Terms
- TRT Terms: Data fusion; Defects; Flaw detection; Railroad tracks; Sensors
- Subject Areas: Data and Information Technology; Maintenance and Preservation; Railroads;
Filing Info
- Accession Number: 01975690
- Record Type: Research project
- Source Agency: Southern Plains Transportation Center
- Contract Numbers: 69A3552348306 (CY3-OSU-07)
- Files: UTC, RIP
- Created Date: Jan 5 2026 11:04PM