ПРИМЕНЕНИЕ ГЛУБОКОГО ОБУЧЕНИЯ ДЛЯ АУГМЕНТАЦИИ И ГЕНЕРАЦИИ ПОДВОДНОГО НАБОРА ДАННЫХ С ПРОМЫШЛЕННЫМИ ОБЪЕКТАМИ

Abstract

The purpose of the study: development of a deep learning method for augmentation and generation of a problem-oriented dataset containing industrial objects, including the development of more efficient data generation and augmentation algorithms based on deep learning, which allow you to create more diverse and realistic data corresponding to industrial objects that can be transferred from one style domain to another. The goal set in the study is related to the actual scientific and technical problem of providing computer vision in systems operating in the underwater environment. These can be autonomous uninhabited underwater vehicles that look for breaks in pipelines, analyze oil leaks, the movement of schools of fish, etc. However, today there is not enough data containing the described objects in the conditions of their real existence. Thus, it is necessary to provide the training sample with realistic images. Research methods: the CycleGAN architecture, which converts a dataset containing images of various objects taken in a laboratory or in a conventional aboveground environment into a dataset containing the cha¬racteristics of an underwater environment. To evaluate the developed augmentation algorithm, it is proposed to use image classification by domains, which can be performed using the ResNet convolutional neural network. Results of the study. A tool is presented to solve the problem of the lack of underwater datasets, a deep learning model is developed, which is used to create images with underwater elements. The model works on the principle of a cyclic generative adversarial network, which receives a real image of an industrial facility in surface conditions as an input, and returns a generated image of the same industrial facility in underwater conditions as an output. Visual analysis of images shows that this method is quite adequate. In addition, a test on the classification model showed almost 100% ability of the neural network to distinguish between domains. Conclusion. The study showed that the CycleGAN model can be used to create images of various objects in the underwater environment. In the future, it is possible to search for additional augmentation procedures, in addition, augmentations of the generated set can be used images, which will also provide researchers and developers with sufficient material with industrial facilities in the underwater environment. This can improve the quality of developments.