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Authors: Kira Maag 1 and Matthias Rottmann 2 ; 3

Affiliations: 1 Ruhr University Bochum, Germany ; 2 University of Wuppertal, Germany ; 3 EPFL, Switzerland

Keyword(s): Deep Learning, Semantic Segmentation, Domain Generalization, Depth Estimation.

Abstract: State-of-the-Art deep neural networks demonstrate outstanding performance in semantic segmentation. However, their performance is tied to the domain represented by the training data. Open world scenarios cause inaccurate predictions which is hazardous in safety relevant applications like automated driving. In this work, we enhance semantic segmentation predictions using monocular depth estimation to improve segmentation by reducing the occurrence of non-detected objects in presence of domain shift. To this end, we infer a depth heatmap via a modified segmentation network which generates foreground-background masks, operating in parallel to a given semantic segmentation network. Both segmentation masks are aggregated with a focus on foreground classes (here road users) to reduce false negatives. To also reduce the occurrence of false positives, we apply a pruning based on uncertainty estimates. Our approach is modular in a sense that it post-processes the output of any semantic segmen tation network. In our experiments, we observe less non-detected objects of most important classes and an enhanced generalization to other domains compared to the basic semantic segmentation prediction. (More)

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Paper citation in several formats:
Maag, K. and Rottmann, M. (2023). False Negative Reduction in Semantic Segmentation Under Domain Shift Using Depth Estimation. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 397-408. DOI: 10.5220/0011607400003417

@conference{visapp23,
author={Kira Maag. and Matthias Rottmann.},
title={False Negative Reduction in Semantic Segmentation Under Domain Shift Using Depth Estimation},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={397-408},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011607400003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - False Negative Reduction in Semantic Segmentation Under Domain Shift Using Depth Estimation
SN - 978-989-758-634-7
IS - 2184-4321
AU - Maag, K.
AU - Rottmann, M.
PY - 2023
SP - 397
EP - 408
DO - 10.5220/0011607400003417
PB - SciTePress

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