TRAFFIC SIGN RECOGNITION WITH CONVOLUTIONAL NEURAL NETWORK
Journal Title: Scientific Journal of Astana IT University - Year 2022, Vol 12, Issue 12
Abstract
Road sign recognition is one of the most important steps drivers can take to avoid dangerous roads or accidents. The purpose of the research work is to develop a recognition system, increasing the classification accuracy of the model, using deep learning methods of Road sign recognition system for drivers in real time on the road. Stages of road sign image classification were carried out, and other authors’ solutions were analyzed. In addition, in this work, the convolutional neural network (CNN) method with the help of deep learning was used to create a system for identifying and recognizing road signs. The proposed road sign recognition system can work in real time to recognize the image of road signs. In this paper, a model is trained using deep learning of 43 different road signs using existing datasets and collected local road signs. A traffic sign detection and recognition system is presented using an 8-layer convolutional neural network, which acquires different functions by training different types of traffic signs. In previous studies, models were trained using simple machine learning algorithms, but the relevance of this study is that a CNN model was trained for a classification task based on convolutional neural networks using deep learning. As a result of the study, classification accuracy of 95% was obtained using deep learning methods. As a novelty of the work, it is possible to note the diversity of the convolutional network methods used to increase the efficiency of the used data set and model training algorithms, the variety of received road signs and algorithms for its recognition, as well as the achievement of a high accuracy rate. This allowed the system to overcome the limited accuracy and performance issues caused by environmental factors, and to be more versatile and accurate than most modern systems.
Authors and Affiliations
Sharipa Temirgaziyeva, Batyrkhan Omarov
Keywords
Related Articles
- EP ID EP713373
- DOI 10.37943/12YZFG6952
- Views 42
- Downloads 0
DEEP LEARNING-BASED FACE MASK DETECTION USING YOLOV5 MODEL
Based on the background of rapid transmission of novel coronavirus and various pneumonia, wearing masks becomes the best solution to effectively reduce the probability of transmission. For a series of problems arising fr...
IMPROVING THE METHOD OF SEARCHING DIGITAL ILLEGAL MEANS OBTAINING INFORMATION BASED ON CLUSTER ANALYSIS
Іn the article the possibilities of the multipositional technology of searching digital insertion devices are investigated based on clustering. Existing means of detecting radiation of digital illegal means obtaining inf...
STRUCTURAL MODEL OF THE SYSTEM OF DEVELOPMENT OF METHODOLOGICAL COMPETENCE OF IT-DISCIPLINE TEACHERS ON THE BASIS OF CONTINUING EDUCATION
The article considers the creation of structural model for the system of development of methodological competence of IT-discipline teachers based on continuing education with the description of the structure of microserv...
MODELLING AGILE-TRANSFORMATION ORGANIZATIONAL DEVELOPMENT PROJECT PORTFOLIO
Agile transformation is a necessary process for companies in various fields of activity to ensure their competitiveness in modern business conditions when the uniformity of production processes and the growth of the le...
DETERMINATION OF PARAMETERS AND THEIR RELATIONSHIPS IN SOCIAL NETWORK ACCOUNTS
The article provides an overview of citizens’ participation in social networks according to the results of 2018 in the Republic of Kazakhstan in comparison with the data of the Statistics Committee. From year to year,...