Recognition of Russian-style road signs using neural networks

Abstract

The problem of creating a model for recognizing objects in images and possible ways to solve it is considered using the example of working with Russian road signs according to ISS R 52290-2004. The analysis of methods for constructing predictive models of image recognition and existing solutions in the public domain is carried out. A convolutional neural network is used as the basic model. A road sign recognition model based on the YOLOv7 transfer network has been developed as a result of training on a dataset from the Russian RTSD road sign image database. The metrics for evaluating the quality of the created model are analyzed and described. The created model meets the quality requirements for objective metrics, allows you to make forecasts taking into account specific situations in different weather conditions and at different times of the day for 146 different predefined classes. The characteristic of the class is the number of the sign according to ISS R 52290-2004. The model has a prediction accuracy of 0.847 with a prediction completeness of 0.811. The average average prediction accuracy of the model is 0.884 when tested on 493 images from the test sample. The test sample does not overlap with the training sample, which consists of 1,842 images. The developed model is published in the public domain both for use for scientific purposes and for further further education. This provides an opportunity for researchers in this field to familiarize themselves with a practical example of the implementation of the model, to supplement or improve it if necessary. The method described in this paper will allow researchers in various subject areas to find a solution that allows them to overcome resource constraints when creating a high-performance and high-quality predictive recognition model.