Improving the transfer learning performances in the classification of the automotive traffic roads signs

Abstract

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.

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