Generating of synthetic datasets using diffusion models for solving computer vision tasks in urban applications

Abstract

The paper illustrates the process of generating synthetic data using diffusion neural networks for solving popular computer vision tasks in urban applications: traffic vehicles detection and classification. The result of this study demonstrated new approach for synthetic datasets creation using modern generative neural networks. To elaborate this approach, experiments were performed to generate synthetic dataset to solve the above basic computer vision problem in urban applications using the most advanced diffusion neural network models (Kandinsky 2.2). Experiments were performed on these datasets to train deep neural networks to solve the object detection problem (YOLOv5). The results of testing the trained detectors on a specially selected validation dataset showed the potential viability of a synthetic dataset generation approach using diffusion neural network models. However, full-fledged use of this approach to generate synthetic datasets that can be used in training deep neural networks of computer vision in production is accompanied by some difficulties, namely high difference between domains of real data and generated data, high labor costs of tuning trained diffusion models to achieve the highest quality of generation, etc.