The article discusses the influence of ANN topology on its efficiency in solving the problem of increasing the image resolution. An empirical approach is used to establish the fact and determine the nature of the influence. At the beginning of the article, the most commonly used topological techniques for constructing an ANN, which are used to solve the problem of increasing the image resolution, are described. Then, the process of creating an ANN is described based on the above topologies. After that, the learning process of ANN is described. A supervised learning algorithm was used to train the networks, and a set of 7 000 images was used as training data. At the end of the article, an assessment of the efficiency of the trained ANN is carried out, through which the effectiveness of topological solutions is determined. To assess the performance of an artificial neural network, a validation dataset of 100 image is used. Two algorithms are used to assess the quality of enhanced images: SSIM and PSNR. The interpretation of the results obtained is also given.
The article is devoted to the development and analysis of methods of identifying dynamic objects. A system for identifying information from a luggage tag based on several neural networks with the SSD InceptionV2 architecture has been developed. These neural networks work with sufficiently high accuracy 82-95% and speed 7-10fps. Advantages and disadvantages of application of method of scale-invariant feature transform for identification of luggage tags are considered. The operability of the methods on real images has been tested.
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