FraunhoferIOSB/Synset-Signset-Germany

Synset Signset Germany

The Synset Signset Germany dataset addresses the task of traffic sign recognition in Germany. It contains a total of 105,500 images of 211 different German traffic sign classes, including newly published (2020) and thus comparatively rare traffic signs. The subset of the first 43 classes in the dataset aims to represent a “synthetic twin” of the well-known GTSRB dataset.

Website: synset.de/datasets/synset-signset-ger/
Paper: Sielemann, A., Loercher, L., Schumacher, M. L., Wolf, S., Roschani, M., Ziehn, J. and Beyerer, J. (2024). Synset Signset Germany: a Synthetic Dataset for German Traffic Sign Recognition. In 2024 IEEE International Conference on Robotics and Automation (ICRA). [arXiv]
Authors: Anne Sielemann, Lena Lörcher, Max-Lion Schumacher, Stefan Wolf, Masoud Roschani, Jens Ziehn, and Juergen Beyerer. Fraunhofer IOSB and Fraunhofer IPA, Germany.
Funded by: Fraunhofer Internal Programs under Grant No. PREPARE 40-02702 within the ML4Safety project and the German Federal Ministry for Economic Affairs and Climate Action, within the program “New Vehicle and System Technologies” as part of the AVEAS research project.
License: CC-BY 4.0

Description

The Synset Signset Germany dataset is a synthetic dataset designed for the task of traffic sign recognition for the country of Germany. It contains a total of 105,500 images of 211 different German traffic sign classes, including newly published (2020) and thus comparatively rare traffic signs. A subset of 43 classes in the dataset aims to represent a “synthetic twin” of the well-known German Traffic Sign Recognition Benchmark (GTSRB), thus it is well-suited for comparing real-world and synthetic data. Thanks to the extensive metadata, it can also be used for applications in the context of explainable AI (XAI) or robustness analyzes and systematic tests.

Our data generation approach is based on the Fraunhofer simulation platform OCTAS® following the general framework of physically-based rendering. It combines the advantages of data-driven and analytical modeling approaches: GAN-based texture generation is employed to produce data-driven dirt and wear artifacts, resulting in unique and realistic traffic sign surfaces. In parallel, analytical scene modulation ensures physically accurate illumination and appropriate geometric transformations, while also enabling fine-grained parameterization of the rendered scenes.

For each of the 211 traffic sign classes, the dataset contains 500 RGB images, accumulating to 105,500 independent images in total. All images were rendered by the rasterization-based engine OGRE as well as by the path tracing engine Cycles. In addition to a sample-wise mask and segmentation image, the dataset also contains extensive metadata, including the stochastically selected environment and imaging effect parameters for each image.

Citation and Reference

To cite this dataset in your scientific work, please use the following bibliography entry:

BibTeX:

@inproceedings{synset_signset_ger_sielemann_2024,
  title={{Synset Signset Germany: A Synthetic Dataset for German Traffic Sign Recognition}},
  author={Sielemann, Anne and Loercher, Lena and Schumacher, Max-Lion and Wolf, Stefan and Roschani, Masoud and Ziehn, Jens and Beyerer, Juergen},
  booktitle={2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)},
  year={2024}
}

APA:

Sielemann, A., Loercher, L., Schumacher, M., Wolf, S., Roschani, M., Ziehn, J., and Beyerer, J. (2024).
Synset Signset Germany: A Synthetic Dataset for German Traffic Sign Recognition.
In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC).

In case of copying and redistributing, or publishing an adapted version of our dataset, please provide the name of our dataset, the creator names, a copyright notice, a link to this website, a license notice with a link to the license, and, if changes were made, a disclaimer notice, and a short description of the applied changes. For example, as follows:

This work is based on the Synset Signset Germany Dataset
by Anne Sielemann, Lena Loercher, Max-Lion Schumacher, Stefan Wolf, Jens Ziehn, Masoud Roschani, and Juergen Beyerer,
© 2024 Fraunhofer IOSB, All rights reserved.
Link: https://synset.de/datasets/synset-signset-ger/
Licence: CC BY 4.0, https://creativecommons.org/licenses/by/4.0/
Disclaimer: The original authors are neither affiliated nor responsible for any applied changes.

Uses

The dataset is designed for the task of traffic sign recognition in Germany.

Direct Use

The dataset is intended for the following use cases:

• Training ML models for the task of German traffic sign recognition
• Analyzing the difference between the synthetic dataset and real-world traffic sign recognition datasets, especially the closely related GTSRB dataset.
• Testing ML models for the task of traffic sign recognition, in particular by using the detailed meta data of each image.

Out-of-Scope Use

The dataset should not be used for critical applications, particularly high-risk applications as named by the European AI Act under Annex III (which includes "AI systems intended to be used for the ‘real-time’ and ‘post’ remote biometric identification of natural persons" and "AI systems intended to be used as safety components in the management and operation of road traffic"), without exhaustive research into the fitness of the dataset, to evaluate whether it is "relevant, sufficiently representative, and to the best extent possible free of errors and complete in view of the intended purpose of the system." No such claim is not made with the publication of this dataset.

Dataset Structure

The dataset is available in two rendering variants:

• Rendering performed by the OCTAS® API to OGRE engine interface.
• Rendering performed by the OCTAS® API to Cycles engine interface.

Both variants include 211 traffic sign classes with 500 images each, leading to a total of 105,500 images.

Each of the 105,500 renderings contains the raw image (i.e., the simulated camera image), a semantic segmentation image, a mask image, and metadata about the traffic sign status (orientation, upper signs, lower signs, etc.), the environment (daytime, contrast, location, etc.), and the imaging effects (noise level, motion blur strength, aec error, etc.).

The dataset provides an exemplary training and validation split.

Dataset Creation

Curation Rationale

The use case of traffic sign recognition has the advantages of, on the one hand, representing a well-understood and established task that provides a wide range of publicly available datasets and applicable models. On the other hand, it remains the subject of active research, in particular, to address challenges such as corner cases and weather conditions, and it has practical relevance, for example, for driver assistance systems, automated driving, and mapping. Since new traffic signs are constantly being released (2020 in Germany) and the coverage of existing signs in publicly available datasets is still limited for a distinction of less common classes, the demand for both training and testing data still persists.

The dataset was designed to be comparable to the GTSRB dataset, one of the best known available traffic sign recognition benchmark datasets.

Furthermore, it was aimed at providing detailed metadata to enable dataset uses such as XAI analyzes and robustness checks.

Source Data

• The dataset was generated in the OCTAS® simulation framework, by using rasterization trough the OGRE engine, as well as path tracing through the Cycles engine developed by the Blender Project.

• The traffic sign template images, which are used as input to the GAN-based texture synthesis, stem from the Wikipedia overview of German traffic signs.

• Image-based lighting (IBL) uses 327 environment maps from PolyHaven.

• The 3D geometry of the tree that serves as an occlusion object originates from PolyHaven.

Who are the source data producers?

• PolyHaven, as the provider of the environment maps for image-based lighting (IBL) and the 3D tree object, is an online library for open (CC0) 3D assets provided by different authors.

• Wikipedia, one of the largest free multilingual open-content encyclopedias, includes the complete list of existing German traffic signs and their template images.

Annotations

Annotation process

The major part of the annotations, including masks, segmentation images, camera parameters and artifacts, and environmental conditions, is based on ground truth data created as part of the scene generation / rendering process. Semantic segmentation images were rendered using the Ogre rendering engine plugin for OCTAS®, which provides rasterization / shading-based image generation.

The only manual annotation performed in the creation of the particular dataset is the labeling of permissible upper and lower signs taking the German traffic code / regulation StVO (Straßenverkehrs-Ordnung) and real-world examples into account.

Who are the annotators?

The annotation of the permissible upper and lower signs was performed by the authors.

Personal and Sensitive Information

The dataset contains no data that might be considered personal, sensitive, or private.

Bias, Risks, and Limitations

• Traffic Signs: The wear and tear generation is limited to artifacts such as color fading, scratches, screw holes, and sticker residues. Complex stickers, graffiti, or dirt are not included. Retroreflector patterns are excluded, and retroreflection is not simulated. The traffic signs are solely mounted on metallic traffic sign poles.

• Environment: Environmental variation includes no adverse weather conditions (snow, raindrops, fog, ...).

• Occlusions: All included occlusions or shadows stem from a single 3D tree geometry.

• Camera: Only one set of intrinsic camera parameters is used, and only a single camera lens type (based on a Tamron M112FM35 35 mm lens) is simulated. It can be assumed that the set of simulated imaging artifacts is not complete.

Recommendations

It is recommended to use the dataset primarily for scientific research. Application to practical real-world use cases should include human oversight and exhaustive evaluation of the fitness for the respective purpose, including the impact of domain shifts.

Dataset Card Contact

Anne Sielemann
Fraunhofer IOSB
Group »Automotive and Simulation«
Fraunhoferstr. | 76131 Karlsruhe | Germany
anne.sielemann@iosb.fraunhofer.de
www.iosb.fraunhofer.de

Jens Ziehn
Fraunhofer IOSB
Group leader »Automotive and Simulation«
Fraunhoferstr. | 76131 Karlsruhe | Germany
Phone +49 721 6091 – 633
jens.ziehn@iosb.fraunhofer.de
www.iosb.fraunhofer.de