Measuring the Effect of Background

Measuring the Effect of Background on Classification and Feature Importance in Deep Learning for AV Perception (Sielemann, Barner et al., 2025)

A major advantage of synthetic data is their parameterizability: our Synset Signset rendering pipeline for generating synthetic traffic sign images allows one to precisely specify the random distributions from which the images are sampled. Additionally, the exact parameter configuration is known for each individual image. Due to these properties, synthetic data are not only suitable for training machine learning (ML) approaches, as demonstrated in our previous work on Synset Signset Germany, but can also be used to systematically test ML approaches or tools, as long as a sufficient degree of realism and variance is ensured. On that basis, this work aims to systematically investigate a common assumption used by explainable AI (XAI) methods.

Common approaches to 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 SHAP¹ and GradCAM² 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. Based on the synthesis pipeline of the Synset Signset Germany dataset, which demonstrated comparability to real-world data, we show how systematically generated synthetic data can test assumptions about feature importance-based XAI and isolate factors between classification quality and XAI values.

Synset Background Effect Datasets

Therefore, 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. Thereby, a correlated background means that each traffic sign is depicted in its most probable environment according to German traffic code / regulation StVO³ (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.

Details

Field	Task	License	Samples	Key Annotations	Institutions
Computer Vision	traffic sign recognition	Creative Commons Attribution 4.0	90,200 images per dataset, 541,200 images in total	Per frame: Class (sign type), environment conditions (e.g., time of day, contrast), imaging artifacts (noise, motion blur, chromatic aberration), semantic segmentation, binary mask.	Fraunhofer IOSB, KIT-IES

Paper and reference

This work is licensed under Creative Commons Attribution 4.0.

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

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. In 2025 IEEE International Automated Vehicle Validation Conference (IAVVC).

@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}
}

In case of copying and redistributing, or publishing an adapted version of our datasets, please provide the name of our dataset, the creator names, a copyright notice, a link to this website, a license notice with 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 Measuring the Effect of Background on Classification and Feature Importance in Deep Learning for AV Perception
by Anne Sielemann, Valentin Barner, Stefan Wolf, Masoud Roschani, Jens Ziehn, and Juergen Beyerer,
© 2025 Fraunhofer IOSB, All rights reserved.
Link: synset.de/datasets/synset-signset-ger/background-effect/
Licence: CC BY 4.0
Disclaimer: The original authors are neither affiliated nor responsible for any applied changes.

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Credits

This work was supported by the Fraunhofer Internal Programs under Grant No. PREPARE 40-02702 within the “ML4Safety” project, as well as funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) within the program “New Vehicle and System Technologies” as part of the AVEAS research project. The real-world data of worn / dirty traffic signs, used to train the GAN for texture synthesis, was acquired with the kind support of the civil engineering department of the city of Karlsruhe, Germany (Tiefbauamt Karlsruhe).

1. S. Lundberg, “A unified approach to interpreting model predictions,” arXiv preprint arXiv:1705.07874, 2017. ↩︎
2. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 618–626. ↩︎
3. Verkehrszeichen Wissensnetz – stvo2Go ↩︎