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Synthetic Training Data Generation for Deep Learning-Based Billet Detection in Rolling Mills

Maria Luschkova; Christian Schorr; Tim Dahmen
In: Proceedings of the International Conference on NDE 4.0. International Conference on NDE 4.0 (NDE 4.0), October 24-27, Berlin, Germany, DGzfP, 2022.


AI-powered quality assurance solutions are gaining momentum in the steel industry under the Industry 4.0 paradigm. In rolling mills, knowing the real-time location of billets, i.e. fast moving bars of hot steel, is important in order to guarantee a safe process and defect-free end products. To achieve this aim, we present a deep learning-based detection of these billets in rolling mills using synthetically generated training data. A core practical challenge for many deep learning projects is the limited availability of appropriate, annotated training data. We propose a method for simulating images employing a partial digital twin of the rolling process. Partial models governing the shape and location of the billets, the layout of the rolling mill floor, the camera settings, and the lighting situation changing over time are combined into a scenario model. Choosing different parametrizations of this scenario model facilitates synthesizing a broad range of images for training. The resulting deep learning model is utilised to detect billets in real-world images from an actual rolling mill. We describe the creation of the partial models using aerial photogrammetry, expert knowledge, and 3D modelling, as well as the choice of the deep learning model. An evaluation of the model's performance on real-word images shows the applicability of our synthetic training data approach.


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