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    Item type:Publication, Access status: with files ,
    Adapting synthetic training data in deep learning-based visual surface inspection to improve transferability of simulations to real-world environments
    (The International Society for Optical Engineering, 2023)
    Schmedemann, Ole
    ;
    Schlodinski, Simon
    ;
    Holst, Dirk
    ;
    Schüppstuhl, Thorsten
    Learning models from synthetic image data rendered from 3D models and applying them to real-world applications can reduce costs and improve performance when using deep learning for image processing in automated visual inspection tasks. However, sufficient generalisation from synthetic to real-world data is challenging, because synthetic samples only approximate the inherent structure of real-world images and lack image properties present in real-world data, a phenomenon called domain gap. In this work, we propose to combine synthetic generation approaches with CycleGAN, a style transfer method based on Generative Adversarial Networks (GANs). CycleGAN learns the inherent structure from real-world samples and adapts the synthetic data accordingly. We investigate how synthetic data can be adapted for a use case of visual inspection of automotive cast iron parts, and show that supervised deep object detectors trained on the adapted data can successfully generalise to real-world data and outperform object detectors trained on synthetic data alone. This demonstrates that generative domain adaptation helps to leverage synthetic data in deep learning-assisted inspection systems for automated visual inspection tasks.
    Publicationtype: Conference Paper
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