## 1 Introduction  
Today, computer vision systems are very important in many industries and fields. In [1], a brief overview of the application of computer vision algorithms in automation and robotics is provided. Technologies that use graphs in computer vision are now being used [2]. In [3] an analysis of combinations of image descriptors that are used was performed, which makes it possible to significantly improve the quality of image processing in relation to the task of analyzing images of industrial parts without textures. The main idea of this process is to use a support vector machine, which, for this narrow class, allowed more efficient processing than that of deep learning models. Nevertheless, it is rightfully believed that a significant leap in the quality of computer vision systems occurred with the advent of deep learning methods [4]. With the growth of computational capabilities, the complexity of convolutional neural networks (CNNs) began to increase. One such breakthrough is considered to be the emergence of the AlexNet network [5], which significantly surpassed all previously known metrics in recognition tasks. The network successfully recognized images from the ImageNet database, where the input images were submitted in a size of 224 by 224 pixels, after which they were sequentially passed through paired convolution and pooling layers, providing feature extraction using convolutional kernels. The authors of [5] managed to achieve great success due to their use of drop out regularization, which, in 2012, had just begun to be used, and was described in detail in [6]. These architectures were further supplemented by next generation CNNs, such as VGG [7], Inception [8], ResNet [9], and Xception [10]. In [7], an architecture with 16-19 weight layers and a small convolution kernel (3 by 3) was proposed. The first version [8] of Inception was a little deeper; namely, it had 22 layers. However, in 2014, it became the state-of-the-art (SOTA) model in image recognition, since the authors managed to choose an architecture that was the most efficient at the time in terms of resource use. In [9], a network with 152 layers was considered, which made it possible to reduce the error rate on the ImageNet dataset to 3.57%. Further improvements [10] allowed the 123-layer network to extract nearly 23 million features from the image. The development of algorithms for detecting objects in images has become a logical extension of the recognition task. The main idea is that it is possible to solve the recognition problem on a limited local area of an image. This is how the R-CNN network was suggested for detection and recognition by [PERSON] [11]. R-CNN proposes an algorithm for the selection of regions or local areas in an image. However, the operation of such a network was rather slow, since it was necessary to perform convolution for each proposed region, most of which were unsuccessful, i.e., they did not contain objects. Moreover, they could partially enclose an object in the form of a bounding rectangle; it is important for the algorithm that the frame bounds the directly detected object, and not be a part of it. Then, the Fast R-CNN architecture was produced [12], where regions were formed directly on a feature map obtained as a result of convolution and pooling. This required reflecting these regions in the original image, and a regression model was applied to refine the bounding box coordinates. The next step in acceleration was the emergence of an algorithm in which the method of forming regions was improved; the Faster R-CNN algorithm [13]. In this architecture, a special neural network is trained, which more efficiently suggests regions for subsequent object detection. Finally, in 2017, the Mask R-CNN architecture appeared in the R-CNN family of architectures [14], which solves, in addition to detection and recognition tasks, segmentation and instance object segmentation tasks.