Partially Connected Neural Networks for an Efficient Classification of Traffic Signs

Road signs recognition plays an important role in improving traffic safety for both drivers and pedestrians. To ensure this recognition, many approaches are proposed by researchers. To overcome the limitations of the existing methods, Deep Learning approaches are used. This type of approaches achieves high recognition performances, and is also less sensitive to real world adverse conditions. However, they are in contrast very computationally expensive due essentially to three main factors, which are more precisely, the size of input images, the type of used layers, and the number of used parameters. From this perspective, the objective of this work is to adopt an approach that aims to reduce this computational complexity, in order to ensure a fast and efficient classification of traffic signs, especially for low and limited resources environments. The adopted approach reaches good classification accuracies, and that by using BTSCD dataset. © 2021 FRUCT.