Object Recognition Based on Three-Dimensional Computer Graphics

This paper considers the issue of training convolutional neural networks based on the representation of a data set in the form of three-dimensional computer graphics. The principle of operation of convolutional neural networks is to train and recognize categories of objects using orthogonal projections of a three-dimensional object. In the recognition process, difficulties arise in mirroring the object, which are solved by using the Pearson correlation coefficient. To use a convolutional neural network, a comparative analysis of the architectures of existing effective image recognition solutions for recording visual information was carried out. For the correct operation of the trained neural network, three-dimensional graphics objects were modeled. After obtaining the desired three-dimensional model, a black-and-white image was obtained, which was used to obtain the contours of the white spots. It is on such black-and-white drawings that the object recognition program is trained. The success of the neural network has been proven experimentally. Thus, it is possible to recognize real objects based on convolutional neural networks trained in virtual space.