The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale

TL;DR

This paper introduces a large, diverse, and finely annotated global traffic sign dataset of 100,000 images, enabling improved detection and classification algorithms for autonomous driving and smart city applications.

Contribution

The paper presents the largest and most diverse traffic sign dataset with detailed annotations, facilitating better training and transfer learning for traffic sign detection and classification.

Findings

Established strong baselines for detection and classification.

Demonstrated dataset diversity improves transfer learning.

Verified dataset effectiveness across various conditions.

Abstract

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.