Machine vision inspection of railroad track

Camargo, Luis Fernando Molina; Resendiz, Esther; Hart, John; Edwards, J. Riley; Ahuja, Narendra; Barkan, Christopher P L

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Machine vision inspection of railroad track

2011-01-10
By Camargo, Luis Fernando Molina ; Resendiz, Esther ; Hart, John

File Language:

English

[PDF - 12.01 MB]

Details

Creators:

Camargo, Luis Fernando Molina ; Resendiz, Esther ; Hart, John ; Edwards, J. Riley ; Ahuja, Narendra ; Barkan, Christopher P L
Corporate Creators:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; University of Illinois at Urbana-Champaign. Department of Civil and Environmental Engineering ; Association of American Railroads
Subject/TRT Terms:
Publication/ Report Number:

NEXTRANS Project No. 0281Y02
Resource Type:

Tech Report
Geographical Coverage:

United States
Corporate Publisher:

NEXTRANS Center (U.S.)
Abstract:

North American Railways and the United States Department of Transportation ; (US DOT) Federal Railroad Administration (FRA) require periodic inspection of railway ; infrastructure to ensure the safety of railway operation. This inspection is a critical, but ; labor-intensive task resulting in large annual operating expenditures and it has limitations ; in speed, quality, objectivity, and scope. A machine vision approach is being developed ; to automate inspection of specific components in the track structure. The machine vision ; system consists of a video acquisition system for recording digital images of track and ; custom designed algorithms to identify defects and symptomatic conditions from these ; images, providing a robust solution to facilitate more efficient and effective track ; inspection. The main focus of the system is the detection of irregularities and defects in ; wood-tie fasteners, rail anchors, and turnout components. An experimental on-track ; image acquisition system has been developed and used to acquire video in the field of ; different track classes. The machine-vision algorithms use a global-to-local component ; recognition approach, in which edge and texture-based detection techniques are used to ; narrow the search area where components are likely to be detected. The system will be ; designed to evaluate the railway infrastructure in accordance with FRA track safety ; regulations, but will be adaptable to railroad-specific track standards. In the future, ; defect analysis and comparison with historical data will enhance the ability for longerterm ; predictive assessment of the health of the track system and its components, more ; informed and proactive maintenance strategies, and improved understanding of track ; structure degradation and failure modes.
Format:

PDF
Funding:

DTRT07-G-005
Download URL:

https://rosap.ntl.bts.gov/view/dot/18644/dot_18644_DS1.pdf
File Type:

[PDF - 12.01 MB]
Collection(s):

University Transportation Centers ; US Transportation Collection
Main Document Checksum:

urn:sha-512:e2fc407dc1bfaa6d2a5b6cf55c8b9b7581148ee5411fa6150d35d687cb7948c8eb3dd204e94047e4bd3d82945555a46e7f58f022a0a3b2a669cab08b4c79ce16

File Language:

English

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