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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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Author of HS ReutlingenCybinski, Steven
Erschienen in:Experience (IT) : Informatik-Konferenz an der Hochschule Reutlingen, 8. Mai 2019. - (Informatics Inside ; 19)
Publisher:Hochschule Reutlingen
Place of publication:Reutlingen
Document Type:Conference proceeding
Publication year:2019
Tag:AprilTags; ArUco; You Only Look Once (YOLO); machine learning; pre-processing; semi-automatic data labelling
Page Number:10
First Page:1
Last Page:10
DDC classes:004 Informatik
Open access?:Ja
Licence (German):License Logo  Open Access