Russian Federation
Moscow, Russian Federation
OKSO 02.04.02 Фундаментальная информатика и информационные технологии
TBK 6021 Кибернетика. Теория игр
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.
algorithms, synthetic data generation, neural network models, detection, image
1. Liu, L., Ouyang, W., Wang, X. et al. Deep Learning for Generic Object Detection: A Survey. Int J Comput Vis 128, 261-318 (2020)
2. Everingham, M., Eslami, S.M.A., Van Gool, L. et al. The PASCAL Visual Object Classes Challenge: A Retrospective. Int J Comput Vis 111, 98-136 (2015)
3. Russakovsky, O., Deng, J., Su, H. et al. ImageNet Large Scale Visual Recognition Challenge. Int J Comput Vis 115, 211-252 (2015)
4. Lin TY. et al. (2014) Microsoft COCO: Common Objects in Context. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision - ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8693. Springer, Cham
5. Felzenszwalb, P.F., Huttenlocher, D.P. Efficient Graph-Based Image Segmentation. International Journal of Computer Vision 59, 167-181 (2004)
6. Achanta, R., et al.: SLIC superpixels. Epfl (2010)
