Synthetic training data generation for visual object identification on load carriers

With visual AI processes relying on individual and context accurate training data, the existing common object datasets and randomization based synthetic data pipelines can only hardly be transferred or applied on specific and narrow industrial tasks. To enable visual AI applications for intralogistics processes, such as supervision or localization of objects, a domain-knowledge driven implementation for generation of context accurate synthetic training data is introduced. With this consideration of process and domain requirements in the data generation pipeline itself, a data-generator for object identification on load carriers is contributed.

Subjects

Production Automation

Intralogistics

Synthetic Training Data

AI Data Generation

Object Identification

DDC Class

600: Technik

620: Ingenieurwissenschaften

Funding(s)

Entwicklung und prototypische Erprobung von intelligenten und modularen Ladungsträgern zur schnelleren und effizienteren Materialversorgung bei der Flugzeugproduktion

Funding Organisations

Bundesministerium für Wirtschaft und Energie - BMWi

More Funding Information

Research was funded by the German Federal Ministry for Economics and Energy under the Program LuFo V-3 DEPOT.

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by-nc-nd/4.0/

Name

1-s2.0-S2212827121011094-main(1).pdf

Size

1.14 MB

Format

Adobe PDF

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Synthetic training data generation for visual object identification on load carriers