Development of synthetic data generation algorithms for training neural network models for detecting objects in an image

The paper is devoted to the development of synthetic data generation algorithms for training models of object detectors in the image. Modern SOTA architectures based on convolutional neural networks, as well as methods for their training, are considered as target models. The features that a training set based on synthetic data must have for the stable operation of the model on a set of natural data are revealed. The proposed methods and principles for generating such data are described. As an accompanying practical example, the problem of detecting commodity items on the shelves of grocery supermarkets is considered, in the context of which the implemented algorithms were tested.