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Machine vision for condition monitoring vegetation on railway embankments
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik. Edinburgh Napier University.ORCID-id: 0000-0003-4812-4988
Edinburgh Napier University.
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
2015 (Engelska)Ingår i: 6th IET Conference on Railway Condition Monitoring (RCM 2014), The Institution of Engineering and Technology (IET) , 2015, s. 3.2.1-3.2.1Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

National Railway Administrations in Northern Europe do not employ systematic procedures in monitoring the current state of vegetation to form the basis of maintenance decision making. Current day vegetation maintenance is largely based on human visual estimates. This paper investigates a machine vision (MV) approach to be able to automatically quantify the amount of vegetation on a given railway section. An investigation assessing the reliability of human estimates is also conducted along the same railway section.A machine vision algorithm was developed and implemented. Initially, the algorithm determines a region of interest (ROI), i.e. the desired monitored area in each collected image. This ROI is dependent on fixed objects in the image, namely the two rails. When the rails are found the algorithm will compute the ROI, which is predetermined by e.g. the railway administrator. After this, a perspective projection correction will be made, and the vegetation will be segmented. Cover is reported as a percentage of the total ROI for each image. Results: The machine vision algorithm is capable of processing 98% of the images. Failure in the remaining 2% of cases is attributed to the algorithms' inability in find the rails within the image. Analysis of variance tests were conducted to compare the observers cover assessments in sample plots. Upon comparing the observers plot wise mean estimates with the machine vision output, results show that the human visual estimates do not correlate with the results reported by the machine vision output. As such, the result indicates that it is very hard to fit human estimates by regression with the machine vision result. Additionally the results show that humans are not in agreement with each other, and often are exaggerating the extent of vegetation cover compared to the machine vision output.The investigation shows that one should be very careful when trusting/interpreting human visual estimates. In conclusion, based on the results, the automated machine vision solution is proposed as complementing, or replacing, manual human inspections serving as a base for vegetation control decisions. Impact: By objectively measuring the quantity of vegetation, the maintenance planning and procurement can be effectively improved over time. A machine vision approach for condition monitoring of vegetation will enable condition based maintenance with prior consideration on issues mainly relevant to vegetation type, quantity and biodiversity.

Ort, förlag, år, upplaga, sidor
The Institution of Engineering and Technology (IET) , 2015. s. 3.2.1-3.2.1
Nyckelord [en]
geotechnical structures; computer vision; land cover; statistical testing; vegetation; condition monitoring; railways;automated machine vision solution;machine vision output;vegetation control decision;systematic procedures;maintenance planning;human visual estimates;condition monitoring vegetation;railway embankments;railway administrator;condition based maintenance;procurement;analysis of variance tests;region of interest;manual human inspections;ROI;railway section;cover assessments;National Railway Administration;machine vision algorithm;vegetation maintenance;maintenance decision making;
Nationell ämneskategori
Teknik och teknologier Data- och informationsvetenskap
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-16761DOI: 10.1049/cp.2014.1001ISBN: 978-1-84919-913-1 (tryckt)OAI: oai:DiVA.org:du-16761DiVA, id: diva2:783508
Konferens
6th IET Conference on Railway Condition Monitoring (RCM 2014), Birmingham 17-18 September 2014
Tillgänglig från: 2015-01-26 Skapad: 2015-01-26 Senast uppdaterad: 2018-01-11Bibliografiskt granskad

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Förlagets fulltexthttp://digital-library.theiet.org/content/conferences/10.1049/cp.2014.1001

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Nyberg, Roger G.

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Nyberg, Roger G.Yella, SirilDougherty, Mark
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