Application of modern computer vision algorithms to manage with the counting of image objects

This article is devoted to the research of convolutional neural network architectures in counting objects in an image. Currently, methods using regression are gaining popularity. In this article in order to solve an object counting task using regression method, modi cations of the reference convolutional neural networks AlexNet, VGG16, and ResNet 50, which were developed for image classi cation, were used. Modi cation presented by replacing the second part of the neural network, which classi es images, with one fully connected layer, consisting of one neuron without activating function. In experiments, modi ed architectures of the reference convolutional networks were initialized as folows: using random initialization of the weights and using pretrainedined weights trained on the ImageNet dataset. The results of experiments, which con rm the performance of the proposed models and the use of the neuroplasticity method to solve the problem using regression are preented. The database of images of bacterial cells was used as training and testing material.