This paper represents a study for the realization of a system based on Artificial Intelligence, which allows the recognition of traffic road signs in an intelligent way, and also demonstrates the performance of Transfer Learning for object classification in general. When systems are trained on the aspects of human visualization (HVS), which helps or generates the same decisions, the construct robust and efficient systems. This allows us to avoid many environmental risks, both for weather conditions, such as cloudy or rainy weather that causes obscured vision of signs, but the main objective is to avoid all road risks that are dangerous to achieve road safety, such as accidents due to non-compliance with traffic rules, both for vehicles and passengers. However, simply collecting road signs in different places does not solve the problem, an intelligent system for classifying road signs is needed to improve the safety of people in its environment. This study proposed a traffic road sign classification system that extracts visual characteristics from a Convolution Neural Network (CNN) classification model. This model aims to assign a class to the image of the road sign through the classifier with the most efficient optimized. Then the evaluation of its effectiveness according to several criteria, using the Confusion Matrix and the classification report, with an in-depth analysis of the results obtained by the images that are taken from the urban world. The results obtained by the system are encouraging in comparison with the systems developed in the scientific literature, for example, the Advanced Driving Assistance Systems (ADAS) of the sector automobile.

In this paper, we improve the performance of Deep Learning (DL) by creating a robust and efficient Convolutional Neural Network (CNN) model. This CNN model will be subjected to detecting and recognizing traffic signs in real-time. We apply several techniques; the first is pre-processing, which includes conversion of color space RGB, then equalization and normalization histogram of the image dataset according to Computer Vision (CV) tools. The second is devoted to Artificial Intelligence (AI), which needs the right choice of a neural layer such convolution layer, or dropout layer, with powerful optimizer as Adam and activation functions such as ReLU and SoftMax. Also, we use the technique of augmentation dataset which characterizes by the function of batch size for each epoch. The results obtained is very satisfactory, the prediction at the average is equal to 98%, which encourages this approach to the integration in Intelligent Transportation Systems (ITS) in the automotive sector.

In this article, we use among and the best-known library is Open Computer Vision we call it for short OpenCV. It is used for image processing, to do all operations we want, to isolate and detect a specific object, which in our case are traffic road signs. We process to find the most efficient methods of detection of traffic road signs. Our objective is to demonstrate the links the elements for optimized and powerful to computer vision algorithms that are easy to use as typing in an image and video processing. Most of the techniques they employed the color selection, edge detection, a region of interest selection, and shapes transformation of detection. Many applications require the recognition of traffic road signs in urban areas. The automation of this task is necessary, for example, ADAS systems, and the application of vision robotics or an autonomous vehicle, consist of recognizing and identifying traffic road signs as quickly as possible with errors to be minimized, in images of their type acquired by an embedded camera on board a vehicle.