Using Digital Twins and Synthetic Data for Training Neural Networks in Quality Control Computer Vision Systems

The possibility of using synthetic data and digital twins for training neural network models that being used in quality control computer vision systems is being explored. A method for generating synthetic images of qualitative and defective products is shown in this paper. Experiments have been carried out to train neural networks on generated images with various configurations of synthetic environments. The possibility and effectiveness of applying the domain randomization method, which purposefully violates the photorealism of training images, is considered. The impact of this method on the accuracy of the resulting neural network used for product defect segmentation is evaluated.