Weizenaehrenerkennungmithilfe neuronaler Netzeund synthetisch generierter Trainingsdaten
Lukas Lucks, Laura Haraké, Lasse Klingbeil
Kapitel/Beitrag aus dem Buch: Heizmann M. & Längle T. 2020. Forum Bildverarbeitung 2020.
Part of
Kapitel/Beitrag aus dem Buch: Heizmann M. & Längle T. 2020. Forum Bildverarbeitung 2020.
This paper investigates the usability of
synthesized training data for the recognition of wheat ears
using neural networks in the context of semantic image segmentation.
For this purpose, detailed scenes of wheat fields
consisting of 3D models with high-resolution textures and defined
material properties are modeled. Afterwards, photo realistic
color images are synthesized, which also contain a binary
image mask with the locations of the ear models. The resulting
image pairs are then used as a training data for two neural networks
(U-Net and DeepLab-V3+). To determine whether these
data allows domain adaptation, the trained networks are evaluated
using real wheat field images. The IoU value of about
69.96 shows that information transfer from the synthesized
images to real images is possible.
Lucks, L et al. 2020. Weizenaehrenerkennungmithilfe neuronaler Netzeund synthetisch generierter Trainingsdaten. In: Heizmann M. & Längle T (eds.), Forum Bildverarbeitung 2020. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.58895/ksp/1000124383-30
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Veröffentlicht am 25. November 2020