Analysis of algorithms and systems FOR road sign recognition
The article deals with the problem of recognition of road signs. To solve it, it is proposed to use the most relevant of the five investigated algorithms (Viola-Jones algorithm, descriptors of local functions, principal methods, component neural networks, convolutional neural networks). The operation principles, advantages and disadvantages of algorithms presented in the article are considered. It takes a huge amount of time to recognize traffic signs in four of the five algorithms studied. Based on the selected algorithm using a neural network model, it is trained and tested. The types of traffic sign recognition systems (Traffic Sign Recognition system, Opel Eye system, Speed Limit Assist system, Traffic Sign Information system) are analyzed. The considered principles of operation, the advantages and disadvantages of the algorithms have been used in the proposed systems for recognition of traffic signs. The comparative characteristics of these systems are presented. The best algorithm for traffic signs recognition has been selected, in which, using a self-learning neural network, the best results are achieved with high accuracy of recognition not only of images (symbols, text, speech, faces, various objects), but also of traffic signs. Based on the analysis of algorithms and systems for recognition of road signs and classification of road signs, the application of convolutional neural networks is proposed, which does not require significant financial costs. The development of a traffic sign recognition system based on a convolutional neural network is a promising direction in the design of automated control systems used in vehicles in order to reduce the number of road accidents.