Sergey V. Ulyanov
Andrey G. Reshetnikov
Kirill V. Koshelev
Received 01 August 2019
Recent advances in digital technology, artificial intelligence and deep machine learning make it possible to resolve problem situations that were previously considered algorithmically unsolvable. The convolutional neural network is a widespread and effective tool for deep learning, with the help of which computer vision problems are successfully solved. The process of classifying images of a convolutional neural network is close to a similar process occurring in the cortex of the human brain. This article discusses the architecture of convolutional neural networks and its application in intelligent robotics. Advantages of convolutional networks are used for recognition with a high degree of invariance to transformations, distortions and scaling. This paper presents a brief description of the modernization of the pattern recognition system based on stereo vision technology. As a result a software recognition module based on a convolutional neural network is presented. An improvement in the quality of recognition through the application of a neural network approach is achieved. The possible consolidation of suggested approach based on quantum soft computing and quantum deep machine learning with quantum neural network IT is discussed.
Pattern recognition, stereovision, convolutional neural network, training with teacher, convolution operation, classification.
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