TRAFFIC SIGN RECOGNITION WITH CONVOLUTIONAL NEURAL NETWORK

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.