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Combined Depth and Semantic Segmentation from Synthetic Data and a W-Net Architecture

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Convolutional Neural Networks; Deep Learning

Authors: Kevin Swingler 1 ; Teri Rumble 1 ; Ross Goutcher 2 ; Paul Hibbard 2 ; Mark Donoghue 2 and Dan Harvey 3

Affiliations: 1 Computing Science and Mathematics, University of Stirling, Stirling, FK9 4LA, U.K. ; 2 Psychology, University of Stirling, Stirling, FK9 4LA, U.K. ; 3 Neuron5, Scotland, U.K.

Keyword(s): Computer Vision, Semantic Segmentation, Monocular Depth Estimation, Synthetic Data.

Abstract: Monocular pixel level depth estimation requires an algorithm to label every pixel in an image with its estimated distance from the camera. The task is more challenging than binocular depth estimation, where two cameras fixed a small distance apart are used. Algorithms that combine depth estimation with pixel level semantic segmentation show improved performance but present the practical challenge of requiring a dataset that is annotated at pixel level with both class labels and depth values. This paper presents a new convolutional neural network architecture capable of simultaneous monocular depth estimation and semantic segmentation and shows how synthetic data generated using computer games technology can be used to train such models. The algorithm performs at over 98% accuracy on the segmentation task and 88% on the depth estimation task.

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Paper citation in several formats:
Swingler, K., Rumble, T., Goutcher, R., Hibbard, P., Donoghue, M., Harvey and D. (2024). Combined Depth and Semantic Segmentation from Synthetic Data and a W-Net Architecture. In Proceedings of the 16th International Joint Conference on Computational Intelligence - NCTA; ISBN 978-989-758-721-4; ISSN 2184-3236, SciTePress, pages 413-422. DOI: 10.5220/0012877500003837

@conference{ncta24,
author={Kevin Swingler and Teri Rumble and Ross Goutcher and Paul Hibbard and Mark Donoghue and Dan Harvey},
title={Combined Depth and Semantic Segmentation from Synthetic Data and a W-Net Architecture},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - NCTA},
year={2024},
pages={413-422},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012877500003837},
isbn={978-989-758-721-4},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - NCTA
TI - Combined Depth and Semantic Segmentation from Synthetic Data and a W-Net Architecture
SN - 978-989-758-721-4
IS - 2184-3236
AU - Swingler, K.
AU - Rumble, T.
AU - Goutcher, R.
AU - Hibbard, P.
AU - Donoghue, M.
AU - Harvey, D.
PY - 2024
SP - 413
EP - 422
DO - 10.5220/0012877500003837
PB - SciTePress

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