Automated Track Change Detection Technology for Enhanced Railroad Safety Assessment

Harrington, Ryan; de Oliveira Lima, Arthur; Edwards, J. Riley; Dersch, Marcus S.; Fox-Ivey, Richard; Laurent, John; Nguyen, Thanh; University of Illinois at Urbana-Champaign; Pavemetrics Systems, Inc.

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Automated Track Change Detection Technology for Enhanced Railroad Safety Assessment

2023-04-01
By Harrington, Ryan ; de Oliveira Lima, Arthur ; Edwards, J. Riley ; ...

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English

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Creators:

Harrington, Ryan ; de Oliveira Lima, Arthur ; Edwards, J. Riley ; Dersch, Marcus S. ; Fox-Ivey, Richard ; ... More +

Harrington, Ryan ; de Oliveira Lima, Arthur ; Edwards, J. Riley ; Dersch, Marcus S. ; Fox-Ivey, Richard ; Laurent, John ; Nguyen, Thanh Less -
Corporate Creators:

University of Illinois at Urbana-Champaign ; Pavemetrics Systems, Inc.

University of Illinois at Urbana-Champaign ; Pavemetrics Systems, Inc. Less -
Corporate Contributors:

United States. Department of Transportation. Federal Railroad Administration. Office of Railroad Policy and Development ; United States. Federal Railroad Administration. Office of Research, Development, and Technology

United States. Department of Transportation. Federal Railroad Administration. Office of Railroad Policy and Development ; United States. Federal Railroad Administration. Office of Research, Development, and Technology Less -
Subject/TRT Terms:

[+]

3D Laser Triangulation Algorithms Artificial Intelligence Change Detection Deep Convolutional Neural Networks Detection And Identification Inspection Neural Networks Operations Railroad Safety Real Time Information Technology-augmented Human Inspection
Publication/ Report Number:

DOT/FRA/ORD-23/13
Resource Type:

Tech Report
Geographical Coverage:

United States
Contracting Officer:

Stuart, Cameron
Corporate Publisher:

United States. Department of Transportation. Federal Railroad Administration
Abstract:

The Rail Transportation and Engineering Center at the University of Illinois at Urbana-Champaign and Railmetrics, Inc. evaluated the use of 3D laser scanning, Deep Convolutional Neural Networks (DCNNs), and change detection technology for railway track safety inspections. Researchers evaluated the potential use of these combined technologies to provide value-added inspection data to traditional track inspection methods. The project was conducted between April 2019 and October 2020. Field testing was completed on the High Tonnage Loop at the Transportation Technology Center in Pueblo, Colorado.
Format:

PDF
Funding:

693JJ619C000004
Collection(s):

Federal Railroad Administration
Main Document Checksum:

[+]

urn:sha256:801651a8ced56700bb6d1d73850731dc9db12f77508084bff98b16fc33077d5b
Download URL:

https://rosap.ntl.bts.gov/view/dot/67233/dot_67233_DS1.pdf
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Automated Track Change Detection Technology for Enhanced Railroad Safety Assessment

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