Abstract

Traffic signs are essential map features for smart cities and navigation. To develop accurate and robust algorithms for traffic sign detection and classification, a large-scale and diverse benchmark dataset is required. In this paper, we introduce a new traffic sign dataset of 105K street-level images around the world covering 400 manually annotated traffic sign classes in diverse scenes, wide range of geographical locations, and varying weather and lighting conditions. The dataset includes 52K fully annotated images. Additionally, we show how to augment the dataset with 53K semi-supervised, partially annotated images. This is the largest and the most diverse traffic sign dataset consisting of images from all over the world with fine-grained annotations of traffic sign classes. We run extensive experiments to establish strong baselines for both detection and classification tasks. In addition, we verify that the diversity of this dataset enables effective transfer learning for existing large-scale benchmark datasets on traffic sign detection and classification. The dataset is freely available for academic research at mapillary.com/dataset/trafficsign.