Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3988
Publisher DOI: 10.1016/j.procir.2021.11.211
Title: Synthetic training data generation for visual object identification on load carriers
Language: English
Authors: Schoepflin, Daniel  
Holst, Dirk  
Gomse, Martin 
Schüppstuhl, Thorsten  
Keywords: Production Automation; Intralogistics; Synthetic Training Data; AI Data Generation; Object Identification
Issue Date: Sep-2021
Publisher: Elsevier
Source: Procedia CIRP 104 : 1257-1262 (2021)
Abstract (english): 
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.
Conference: 54th CIRP Conference on Manufacturing Systems, CMS 2021 
URI: http://hdl.handle.net/11420/11203
DOI: 10.15480/882.3988
ISSN: 2212-8271
Journal: Procedia CIRP 
Institute: Flugzeug-Produktionstechnik M-23 
Document Type: Article
Project: Entwicklung und prototypische Erprobung von intelligenten und modularen Ladungsträgern zur schnelleren und effizienteren Materialversorgung bei der Flugzeugproduktion 
Funded by: 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.
Peer Reviewed: Yes
License: CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives) CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
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