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005	20250415123934
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008	250409t20242024oncado  ob   f|0| 0 eng d
040	|aCaOODSP|beng|erda|cCaOODSP
043	|an-cn---
086	1 |aNR16-480/2024E-PDF
100	1 |aLiu, Yan, |eauthor.
245	10|aTechnical report : |bassessing the effectiveness of vision technologies for railcar inspection / |cprepared for: Transport Canada's Innovation Center; prepared by: Yan Liu, Abdelhamid Mammeri, Samy Metari, Md Atiqur Rahman, Alireza Roghani, National Research Council Canada; Michael Hendry, Lianne Lefsrud, Parth Rana, Fereshteh Sattari, University of Alberta.
246	30|aAssessing the effectiveness of vision technologies for railcar inspection
250	|aVersion 1.0.
264	1|a[Ottawa] : |bNational Research Council Canada = Conseil national de recherches du Canada, |cDecember 02, 2024.
264	4|c©2024
300	|a1 online resource (89 pages) : |billustrations, graphs, photographs
336	|atext|btxt|2rdacontent
337	|acomputer|bc|2rdamedia
338	|aonline resource|bcr|2rdacarrier
500	|a"Project: A1-020979."
500	|a"Report number: AST-2024-0054."
504	|aIncludes bibliographical references (pages 66-71).
520	|a"Railways globally are leveraging machine vision technologies to enhance railcar inspections, aiming to improve efficiency and safety. Canadian Pacific Kansas City (CPKC) is a leader in North America, utilizing a process called Portal Office Inspection (POI) ... These findings are then communicated to field inspectors for validation and necessary actions. This innovative approach allows inspections to be performed while trains are in motion, reducing idle times and improving operational efficiency"--Executive summary, page 10.
650	0|aRailroad cars|xInspection|zCanada.
650	0|aTechnology assessment|zCanada.
650	6|aChemins de fer|xWagons|xInspection|zCanada.
650	6|aTechnologie|xÉvaluation|zCanada.
710	2 |aNational Research Council of Canada, |eissuing body.
856	40|qPDF|s2.81 MB|uhttps://publications.gc.ca/collections/collection_2025/cnrc-nrc/NR16-480-2024-eng.pdf