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Novel Synthetic Data Tool for Data-Driven Cardboard Box Localization

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Artificial Neural Networks and Machine Learning – ICANN 2023 (ICANN 2023)

Abstract

Application of neural networks in industrial settings, such as automated factories with bin-picking solutions requires costly production of large labeled datasets. This paper presents an automatic data generation tool with a procedural model of a cardboard box. We briefly demonstrate the capabilities of the system, and its various parameters and empirically prove the usefulness of the generated synthetic data by training a simple neural network. We make sample synthetic data generated by the tool publicly available.

Supported by the TERAIS project in the framework of the program Horizon-Widera-2021 of the European Union under the Grant agreement number 101079338.

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Notes

  1. 1.

    https://www.nvidia.com/en-us/omniverse/solutions/digital-twins/.

  2. 2.

    https://docs.blender.org/api/current/info_advanced_blender_as_bpy.html.

  3. 3.

    http://www.st.fmph.uniba.sk/~gajdosech2/icann2023-dataset/.

  4. 4.

    https://www.photoneo.com/phoxi-3d-scanner/.

References

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Correspondence to Peter Kravár .

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Kravár, P., Gajdoech, L., Madaras, M. (2023). Novel Synthetic Data Tool for Data-Driven Cardboard Box Localization. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14254. Springer, Cham. https://doi.org/10.1007/978-3-031-44207-0_50

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  • DOI: https://doi.org/10.1007/978-3-031-44207-0_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44206-3

  • Online ISBN: 978-3-031-44207-0

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