Synset Signset Germany: a Synthetic Dataset for German Traffic Sign Recognition
This provides a dataset for researchers and developers in autonomous driving to improve traffic sign recognition, especially for rare signs, with applications in explainable AI and robustness testing, though it is incremental as it builds on existing synthetic data methods.
The authors tackled the problem of limited training data for German traffic sign recognition by creating a synthetic dataset with 105,500 images across 211 classes, combining GAN-based texture generation for realistic dirt and wear with analytical scene modulation for physically correct lighting, and demonstrated its realism by evaluating against real-world and synthetic benchmarks.
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.