Abstract

Classifying traffic signs is an indispensable task for autonomous driving systems. Depending on the country, traffic signs possess a wide variability in their visual appearance making it harder for classification systems to succeed. Either the classifier should be fine-tuned or a bigger collection of images should be used. In this paper, we introduce a real-world European dataset for traffic sign classification. The dataset is composed of traffic sings from six European countries: Belgium, Croatia, France, Germany, The Netherlands, and Sweden. It gathers publically available datasets and complements French traffic signs with images acquired in Belfort with the equipped university autonomous vehicle. It is composed of more than 80000 images divided in 164 classes that at the same time belong to four main categories following the Vienna Convention of Road Signs. We analyzed the intra variability of classes and compared the classification performance of five convolutional neural network architectures.