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A machine learning approach for recognising woody plants on railway trackbeds
Högskolan Dalarna, Akademin Industri och samhälle, Informatik.ORCID-id: 0000-0003-4812-4988
2016 (engelsk)Inngår i: International Conference on Railway Engineering (ICRE 2016), 2016Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The purpose of this work in progress study was to test the concept of recognising plants using images acquired by image sensors in a controlled noise-free environment. The presence of vegetation on railway trackbeds and embankments presents potential problems. Woody plants (e.g. Scots pine, Norway spruce and birch) often establish themselves on railway trackbeds. This may cause problems because legal herbicides are not effective in controlling them; this is particularly the case for conifers. Thus, if maintenance administrators knew the spatial position of plants along the railway system, it may be feasible to mechanically harvest them. Primary data were collected outdoors comprising around 700 leaves and conifer seedlings from 11 species. These were then photographed in a laboratory environment. In order to classify the species in the acquired image set, a machine learning approach known as Bag-of-Features (BoF) was chosen. Irrespective of the chosen type of feature extraction and classifier, the ability to classify a previously unseen plant correctly was greater than 85%. The maintenance planning of vegetation control could be improved if plants were recognised and localised. It may be feasible to mechanically harvest them (in particular, woody plants). In addition, listed endangered species growing on the trackbeds can be avoided. Both cases are likely to reduce the amount of herbicides, which often is in the interest of public opinion. Bearing in mind that natural objects like plants are often more heterogeneous within their own class rather than outside it, the results do indeed present a stable classification performance, which is a sound prerequisite in order to later take the next step to include a natural background. Where relevant, species can also be listed under the Endangered Species Act.

sted, utgiver, år, opplag, sider
2016.
Emneord [en]
feature extraction; image classification; learning (artificial intelligence); mechanical engineering computing; railways
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-23467DOI: 10.1049/cp.2016.0513ISBN: 978-1-78561-292-3 (tryckt)OAI: oai:DiVA.org:du-23467DiVA, id: diva2:1049345
Konferanse
IET, International Conference on Railway Engineering (ICRE 2016), Brussels, Belgium, 12-13 May 2016
Tilgjengelig fra: 2016-11-24 Laget: 2016-11-24 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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