Deep Learning Approach for U.S. Traffic Sign Recognition

Abstract

Advanced Driver Assistant Systems (ADAS) have seen massive improvements in recent years; from detecting pedestrians, road lanes, traffic signs and signals, and vehicles to recognizing and tracking traffic signs. Traffic sign recognition systems are used to detect and classify the traffic signs. This research is focused on the classification aspect of the ADAS; i.e. identifying the class a traffic sign belongs to. Most of the current ADAS that use the U.S. traffic signs are limited to speed limit signs recognition only. This work seeks to expand the corridors of U.S. traffic signs recognition to cover all the publicly available classes. The research adopts the VGGNet architecture modified to classify U.S. traffic signs provided by the LISA TS benchmark. The original VGGNet was used to classify The German Traffic Sign Recognition Benchmark and reported an accuracy of 98.7%. This work recorded a validation accuracy of 99.04%.