The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale
This dataset addresses the problem of limited diversity in traffic sign benchmarks for researchers and developers in autonomous driving, though it is incremental as it builds on existing datasets by expanding scale and global coverage.
The authors tackled the need for a large-scale, diverse traffic sign dataset for autonomous driving and smart cities by introducing the Mapillary Traffic Sign Dataset, which includes 100K street-level images from around the world with over 300 manually annotated classes, establishing strong baselines for detection and classification tasks.
Traffic signs are essential map features globally in the era of autonomous driving and smart cities. 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 traffic sign benchmark dataset of 100K street-level images around the world that encapsulates diverse scenes, wide coverage of geographical locations, and varying weather and lighting conditions and covers more than 300 manually annotated traffic sign classes. The dataset includes 52K images that are fully annotated and 48K images that are partially annotated. This is the largest and the most diverse traffic sign dataset consisting of images from all over world with fine-grained annotations of traffic sign classes. We have run extensive experiments to establish strong baselines for both the detection and the classification tasks. In addition, we have verified 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: https://www.mapillary.com/dataset/trafficsign.