Traffic Signs Detection and Classification for European Urban Environments

Traffic signs play an important role for Advanced Driver Assistance Systems (ADAS) as well as for autonomous driving vehicles. Most of the works done focus on recognizing symbol based signs leaving apart important information provided by other type of signs like complementary panels or text based signs. In this paper, we include detection and classification of both symbol and text based signs focusing on the most common ones found in European urban environments. The system consists of three stages, traffic sign detection, refinement and classification. The detection and refinement is performed using Mask R-CNN while the classification is achieved with a proposed Convolutional Neural Network (CNN) architecture. We introduced the extended version of the German Traffic Sign Detection Benchmark (GTSDB), labeled in a pixel manner (masks) with 164 classes grouped into 8 categories. It is used for the detection and classification steps. Experimental results on German environments show that our proposed system is capable of detecting all categories of traffic signs while at the same time recognizing them with high accuracy achieving comparable performance with the state of the art.