INFORMATION TECHNOLOGY FOR RECOGNITION OF ROAD SIGNS USING A NEURAL NETWORK - Elena Yashina; Roman Artiukh; Nikolai Рan; Andrei Zelensky (Сучасний стан наукових досліджень та технологій в промисловості)
The subject of the study is the methods and tools for automation of recognition of road signs at the level of software implementation. Detection of road signs is associated with the processing of a significant amount of video data in real time, which requires significant computing power. Therefore, the purpose of the work is to automate the process of recognition of road signs for filling the databases of navigators, which will allow operatively provide drivers with up-to-date information on established road signs. The following tasks are solved: analysis of methods and software for image recognition; development of the search algorithm for characters in the video frame; implementation of the definition of the contour of the sign; realization of a convolutional neural network for recognition of a sign; testing of applied information technology work. Methods are used: convolutional neural networks;  Viola-Jones's method for recognizing objects in an image, the Bousting method as a way to accelerate the recognition process with a large amount of information. Results: Different approaches to the identification of symbols on images, various software tools for object recognition, image transformation for optimal fragment are considered. An algorithm for detecting and recognizing the sign is developed. Using the Viola-Jones method, a fast way to calculate the values of attributes using the integral representation of an image is implemented. The recognition process takes place by constructing a convolutional neural network. Features of the layers of the roller network are considered.  Schematically illustrated script recognition. The process of interaction of the system with different data sources is represented by a diagram of precedents. The main result is the creation of information technology for the automated recognition of road signs. The algorithm of its work is presented in the form of a sequence diagram. Conclusions. Using the applied application information technology, recognition of road signs is made with an average probability of 88%, which allows automating the process of filling the database of navigators to a large extent, to increase the reliability and productivity of the given process.