README.md

---
license: cc-by-4.0
task_categories:
- image-classification
language:
- en
tags:
- traffic sign recognition
- effect of background
- background correlation
- XAI
- synthetic
- synset
- OCTAS
pretty_name: Synset Background Effect Datasets
---

<img src="synset-background-effect-datasets-title-image.png" width=100% />

# Synset Background Effect Datasets

<!-- Provide a quick summary of the dataset. -->
For investigating the effect of background on feature importance and classification performance, we systematically generated six synthetic datasets for the 
task of traffic sign recognition, which differ only in their degree of camera variation and background correlation. Each of these datasets contains 82 classes 
of traffic signs with 1,100 images per class, resulting in 90,200 images per dataset, summing up to a total of 541,200 images.

**Website**: [synset.de/datasets/synset-signset-ger/background-effect/](https://synset.de/datasets/synset-signset-ger/background-effect/) <br>
**Paper:** Sielemann, A., Barner, V., Wolf, S., Roschani, M., Ziehn, J. and Beyerer, J. (2025). [Measuring the Effect of Background on Classification and Feature Importance in Deep Learning for AV Perception](https://ieeexplore.ieee.org/abstract/document/11219547). In 2025 IEEE International Automated Vehicle Validation Conference (IAVVC). [[arXiv](https://arxiv.org/abs/2512.05937)]  <br>
**Authors:** [Anne Sielemann](https://www.linkedin.com/in/anne-sielemann-23011026a/), Valentin Barner, [Stefan Wolf](https://www.linkedin.com/in/stefan-wolf-2552211a9/),
Masoud Roschani, [Jens Ziehn](https://www.linkedin.com/in/jrziehn/), and Juergen Beyerer. [Fraunhofer IOSB](https://www.iosb.fraunhofer.de/), Germany.  <br>
**Funded by:** [Fraunhofer](https://www.fraunhofer.de/en.html) Internal Programs under Grant No. PREPARE 40-02702 within the ML4Safety project and the [German Federal Ministry for Economic Affairs and Climate Action](https://www.bundeswirtschaftsministerium.de/Navigation/EN/Home/home.html), within the program “New Vehicle and System Technologies” as part of the [AVEAS](https://aveas.org/) research project. <br>
**License:** CC-BY 4.0 <br>

## Description

Common approaches to explainable AI (XAI) for deep learning (DL)-based image classification focus on analyzing the importance of input features on the classification task 
in a given model: saliency methods like SHAP and GradCAM are used to measure the impact of spatial regions of the input image on the classification result. Combined with 
ground truth information about the location of the object in the input image, for example, a binary mask, it is determined whether object pixels had a high impact on the 
classification result, or whether the classification focused on background pixels. The former is considered to be a sign of a healthy classifier, whereas the latter is 
assumed to suggest overfitting on spurious correlations.

A major challenge, however, is that these intuitive interpretations are difficult to test quantitatively, and hence the output of such explanations lacks explanation 
itself. One particular reason is that correlations in real-world data are difficult to avoid, and whether they are spurious or legitimate is debatable. To shed light on 
this issue and test whether feature importance-based XAI reliably distinguishes between true learning and problematic overfitting, we utilize the task of traffic sign 
recognition and systematically generated six synthetic datasets, which only differ in their degree of camera variation and background correlation. Thereby, a correlated 
background means that each traffic sign is depicted in its most probable environment according to German traffic code / regulation StVO (Straßenverkehrs-Ordnung) 
categorized in "urban", "nature", and "urban and nature". A traffic sign warning of wildlife crossing is, for example, likely to be set up on a rural road with natural 
background, while a sign warning of pedestrians is probable to be placed in an urban context. An uncorrelated background, however, means that the background is randomly 
chosen and thus not semantically linked to the depicted traffic sign class.

For dataset generation, we utilized our parameterizable rendering pipeline from our work on the <em>Synset Signset Germany</em> dataset. The pipeline is based on the 
Fraunhofer simulation platform [OCTAS](https://octas.org/). The dataset consists of six subdatasets: correlated and uncorrelated backgrounds cross the camera variation 
stages frontal, medium and high. Each of these datasets contains 82 classes of traffic signs with 1,100 images per class, resulting in 90,200 images per dataset, summing 
up to a total of 541,200 images. The images were rendered with the rasterization-based engine [OGRE3D](https://www.ogre3d.org/).

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

    @inproceedings{measuring_effect_of_background_sielemann_2025,
      title={{Measuring the Effect of Background on Classification and Feature Importance in Deep Learning for AV Perception}},
      author={Sielemann, Anne and Barner, Valentin and Wolf, Stefan and Roschani, Masoud and Ziehn, Jens and Beyerer, Juergen},
      booktitle={2025 IEEE International Automated Vehicle Validation Conference (IAVVC)},
      year={2025}
    }


**APA:**

Sielemann, A., Barner, V., Wolf, S., Roschani, M., Ziehn, J., and Beyerer, J. (2025). <br>
Measuring the Effect of Background on Classification and Feature Importance in Deep Learning for AV Perception. <br>
In 2025 IEEE International Automated Vehicle Validation Conference (IAVVC).

## Uses

The dataset was designed for the investigation of the effect of background correlations on the classification performance and the spatial distribution of important 
classification features within the task of traffic sign recognition.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

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.

## Bias, Risks, and Limitations

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

<!-- Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. -->

It is recommended to use the dataset primarily for scientific research. Application to practical real-world use cases should include
human oversight and the 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](https://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](https://www.iosb.fraunhofer.de)