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Synthetic data generation for bridging Sim2Real gap in a production environment
Citation Link: https://doi.org/10.15480/882.9019
Publikationstyp
Preprint
Date Issued
2023
Sprache
English
Author(s)
TORE-DOI
Synthetic data is being used lately for training deep neural networks in computer vision applications such as object detection, object segmentation and 6D object pose estimation. Domain randomization hereby plays an important role in reducing the simulation to reality gap. However, this generalization might not be effective in specialized domains like a production environment involving complex assemblies. Either the individual parts, trained with synthetic images, are integrated in much larger assemblies making them indistinguishable from their counterparts and result in false positives or are partially occluded just enough to give rise to false negatives. Domain knowledge is vital in these cases and if conceived effectively while generating synthetic data, can show a considerable improvement in bridging the simulation to reality gap. This paper focuses on synthetic data generation procedures for parts and assemblies used in a production environment. The basic procedures for synthetic data generation and their various combinations are evaluated and compared on images captured in a production environment, where results show up to 15% improvement using combinations of basic procedures. Reducing the simulation to reality gap in this way can aid to utilize the true potential of robot assisted production using artificial intelligence.
Subjects
object detection
photorealistic rendering
production
sim2real gap
synthetic data
DDC Class
620: Engineering
670: Manufacturing
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Name
2311.11039.pdf
Size
6.13 MB
Format
Adobe PDF