Abstract
Solving complex computer vision tasks by deep learning techniques rely on large amounts of (supervised) image data, typically unavailable in industrial environments. Consequently, the lack of training data is beginning to impede the successful transfer of state-of-the-art computer vision methods to industrial applications. We introduce BlendTorch, an adaptive Domain Randomization (DR) library, to help create infinite streams of synthetic training data. BlendTorch generates data by massively randomizing low-fidelity simulations and takes care of distributing artificial training data for model learning in real-time. We show that models trained with BlendTorch repeatedly perform better in an industrial object detection task than those trained on real or photo-realistic datasets.
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Acknowledgements
This work was supported by the strategic economic and research program “Innovatives OÖ 2020” of Upper Austria and by the European Union in cooperation with the State of Upper Austria within the project Investition in Wachstum und Beschftigung (IWB).
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Heindl, C., Brunner, L., Zambal, S., Scharinger, J. (2021). BlendTorch: A Real-Time, Adaptive Domain Randomization Library. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_39
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DOI: https://doi.org/10.1007/978-3-030-68799-1_39
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