Development of a deep learning model for recognising traffic sings focused on difficult cases

The automotive industry is expanding its efforts to develop new techniques for increasing the level of intelligent driving and create new autonomous cars capable of driving with more intelligent capabilities. Thus, companies in this sector are turning to the development of autonomous cars and more specifically developing software along with more artificial intelligent algorithms. However, to be able to trust these systems, they must be developed very carefully, and use techniques that can increase the level of recognition that will consequently improve the level of safety. One of the most important components in this respect for road users is the correct interpretation of traffic sings. This paper presents a deep learning model based on convolutional neural networks and image processing that can be used to improve the recognition of traffic sings autonomously. The results are focused on difficult cases such as images with lighting problems, blurry traffic sings, hidden traffic sings, and small images. Hence, real cases are used in this study for identifying the existing problems and achieving good performance in traffic signal recognition. Finally, as a result, the configuration of the neural architecture based on three phases of convolutions proposed shows a validation accuracy of 99.3% during the data training. Another comparison carried out with the model ResNet-50 obtained an accuracy of 88.5%. Thus, for this type of application, a high validation accuracy is required as the results of our model demonstrated