Open AccessAPPLYING NEURAL NETWORKS IN RECOGNIZING CONDITIONALLY GRAPHICAL ELECTRICAL SYMBOLS
Author(s) -
П. П. Алексеев,
Irina Kvyatkovskaya
Publication year - 2021
Publication title -
vestnik astrahanskogo gosudarstvennogo tehničeskogo universiteta. seriâ: upravlenie, vyčislitelʹnaâ tehnika i informatika
Language(s) - English
Resource type - Journals
eISSN - 2224-9761
pISSN - 2072-9502
DOI - 10.24143/2072-9502-2021-2-47-56
Subject(s) - computer science , artificial intelligence , convolutional neural network , artificial neural network , pooling , cognitive neuroscience of visual object recognition , object (grammar) , set (abstract data type) , interactivity , pattern recognition (psychology) , invariant (physics) , machine learning , multimedia , physics , mathematical physics , programming language
The article discusses the issue of using artificial neural networks for recognizing the conditionally graphical designations of electrical engineering, in particular, the convolutional neural networks and the R-CNN object recognition model, which is most suitable for solving the task at hand. Recognition of images of a specific picture is a task set for the complex information processing systems, as well as control and decision-making systems. The classification of various technological or natural objects, analog and digital signals is developed by a set of specific characteristics and properties. Defining the type and features of an object finds its application in different branches of science: machine learning, diagnostics, meteorology, video surveillance and security systems, in virtual reality systems and image search. However, research has not yet been carried out for solving the applied problems and achieving the required parameters (e.g. in recognizing conditional graphical symbols of electrical engineering). The neural networks have been found to have the highest quality and most promising among all mathematical models and methods of pattern recognition. As for the interactivity, the output result of image recognition work is a necessary and sufficient answer, which does not have a stable work on the variability of objects within categories and their invariant transformations. The scheme of the model R-CNN has been studied in detail, the importance of the training sample and its influence on the quality of pattern recognition by the neural network have been grounded. The application of the RoI Pooling method for object recognition in the image is shown in general, due to which there have been selected several regions of interest indicated through the bounding boxes.