Paper Info
Title
Enhanced rail component detection and consolidation for rail track inspection
Abstract
For safety purposes, railroad tracks need to be inspected on a regular basis for physical defects or design noncompliances. Such track defects and non-compliances, if not detected in a timely manner, may eventually lead to grave consequences such as train derailments. In this paper, we present a real-time automatic vision-based rail inspection system, with main focus on anchors - an important rail component type, and anchor-related rail defects, or exceptions. Our system robustly detects important rail components including ties, tie plates, anchors with high accuracy and efficiency. Detected objects are then consolidated across video frames and across camera views to map to physical rail objects, by combining the video data streams from all camera views with GPS information and speed information from the distance measuring instrument (DMI). After these rail components are detected and consolidated, further data integration and analysis is followed to detect sequence-level track defects, or exceptions. Quantitative analysis performed on a real online field test conducted on different track conditions demonstrates that our system achieves very promising performance in terms of rail component detection, anchor condition assessment, and compliance-level exception detection. We also show that our system outperforms another advanced rail inspection system in anchor detection.
Year
DOI
Venue
2012
10.1109/WACV.2012.6163021
WACV
Keywords
Field
DocType
rail component,enhanced rail component detection,rail component detection,anchor-related rail defect,camera view,real-time automatic vision-based rail,physical rail object,advanced rail inspection system,inspection system,detects important rail component,important rail component type,rail track inspection,computer vision,data integrity,real time systems,global positioning system,inspection,real time,edge detection,quantitative analysis
Data integration,Rail inspection,Computer vision,Data stream mining,Computer science,Track (rail transport),Derailment,Artificial intelligence,Global Positioning System,Consolidation (soil),Condition assessment
Conference
ISSN
ISBN
Citations 
1550-5790 E-ISBN : 978-1-4673-0232-6
978-1-4673-0232-6
12
PageRank 
References 
Authors
1.62
4
5
Name
Order
Citations
PageRank
Hoang Trinh1497.09
Norman Haas210046.34
Ying Li317519.80
Charles Otto4192.26
Sharath Pankanti53542292.65