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Semi-automated image data labelling using AprilTags as a pre-processing step for machine learning

  • Data labelling is a pre-processing step to prepare data for machine learning. There are many ways to collect and prepare this data, but these are usually associated with a greater effort. This paper presents an approach to semi-automated image data labelling using AprilTags. The AprilTags attached to the object, which contain a unique ID, make it possible to link the object surfaces to a particular class. This approach will be implemented and used to label data of a stackable box. The data is evaluated by training a You Only Look Once (YOLO) net, with a subsequent evaluation of the detection results. These results show that the semi-automatically collected and labelled data can certainly be used for machine learning. However, if concise features of an object surface are covered by the AprilTag, there is a risk that the concerned class will not be recognized. It can be assumed that the labelled data can not only be used for YOLO, but also for other machine learning approaches.

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Metadaten
Name:Cybinski, Steven
URN:urn:nbn:de:bsz:rt2-opus4-27139
URL:https://infoinside.reutlingen-university.de/?page_id=31
Erschienen in:Informatics inside : experience (IT) : Informatik-Konferenz an der Hochschule Reutlingen, 8. Mai 2019. - (Informatics inside ; 19)
Publisher:Hochschule Reutlingen, INF - Informatik
Place of publication:Reutlingen
Editor:Uwe Kloos
Document Type:Conference Proceeding
Language:English
Year of Publication:2019
Tag:AprilTags; ArUco; You Only Look Once (YOLO); machine learning; pre-processing; semi-automatic data labelling
Pagenumber:10
First Page:1
Last Page:10
Catalogue entry:Im Katalog der Hochschule Reutlingen ansehen
Dewey Decimal Classification:004 Informatik
Open Access:Ja
Licence (German):License Logo  Creative Commons - CC BY - Namensnennung 4.0 International