Synset Signset Germany: a Synthetic Dataset for German Traffic Sign Recognition

TL;DR

This paper introduces Synset Signset Germany, a synthetic dataset for German traffic sign recognition that combines realistic textures and physically accurate scene parameters, supporting AI explainability and robustness testing.

Contribution

It presents a novel synthesis pipeline that generates a large, diverse, and realistic traffic sign dataset with detailed metadata for advanced AI applications.

Findings

Synthetic dataset achieves high realism on GTSRB benchmark

Enables sensitivity analysis through parameter variation

Contains 105,500 images of 211 traffic sign classes

Abstract

In this paper, we present a synthesis pipeline and dataset for training / testing data in the task of traffic sign recognition that combines the advantages of data-driven and analytical modeling: GAN-based texture generation enables data-driven dirt and wear artifacts, rendering unique and realistic traffic sign surfaces, while the analytical scene modulation achieves physically correct lighting and allows detailed parameterization. In particular, the latter opens up applications in the context of explainable AI (XAI) and robustness tests due to the possibility of evaluating the sensitivity to parameter changes, which we demonstrate with experiments. Our resulting synthetic traffic sign recognition dataset Synset Signset Germany contains a total of 105500 images of 211 different German traffic sign classes, including newly published (2020) and thus comparatively rare traffic signs. In addition to a mask and a segmentation image, we also provide extensive metadata including the stochastically selected environment and imaging effect parameters for each image. We evaluate the degree of realism of Synset Signset Germany on the real-world German Traffic Sign Recognition Benchmark (GTSRB) and in comparison to CATERED, a state-of-the-art synthetic traffic sign recognition dataset.