With synthetic data towards part recognition generalized beyond the training instances
Paul Koch, Marian Schlüter, Jörg Krüger · AIP conference proceedings · 2024
In this work we investigate the effect of using synthetic data, generated in a simulation, in order to pre-train an AI-based image classification for industrial components.After pre-training we use real camera-captured training images to fine-tune the AI with the aim to close the Sim2Real domain gap.We compare our approach to purely using real training images of a single candidate object instance.In an exemplary case study for screw recognition, we found that a given AI classification algorithm dropped its recognition rate from 99.8% to 88.5% when testing the algorithm with known and unknown screw instances of the learned object classes, respectively.Employing our pre-training method on the basis of synthetic data, the drop in recognition rate is decreased from 99% to 96.95%.Thus, our proposed method has only a relative drop of 2.05% when shifting towards a generalized domain (including unknown part instances), while a compared approach on the basis of real camera-captured data showed a drop of 11.3%.