loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Marie-Claire Iatrides 1 ; 2 ; Petra Gomez-Krämer 1 ; Olfa Ben Ahmed 3 and Sylvain Marchand 1

Affiliations: 1 L3i Laboratory, La Rochelle University, La Rochelle, France ; 2 Association Ferrocampus, Saintes, France ; 3 Xlim Institute of Research, Poitiers University, Poitiers, France

Keyword(s): Lightweight CNN, Resource-Efficient ML, Semantic Segmentation, Deep Learning, Train Environment.

Abstract: In this paper, we present a promising application of scaling techniques for segmentation tasks in a railway environment context to highlight the advantages of task specific models tailored for on-board train use. Smaller convolutional neural networks (CNNs) do not focus on accuracy but resource efficiency. Our models are scaled using skip connections as well as quantization in order to form lightweight models trained specifically for our context. The proposed models have been evaluated both in terms of segmentation performance and efficiency on state of the art scene segmentation datasets namely RailSem19 and Cityscapes. We have obtained models with less than 3.5M parameters and a minimum of 78.4% of segmentation accuracy showing that lightweight models can effectively segment the railway surroundings.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.15.223.236

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Iatrides, M.-C., Gomez-Krämer, P., Ben Ahmed, O. and Marchand, S. (2025). Towards Resource-Efficient Deep Learning for Train Scene Semantic Segmentation. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3; ISSN 2184-4321, SciTePress, pages 347-354. DOI: 10.5220/0013139000003912

@conference{visapp25,
author={Marie{-}Claire Iatrides and Petra Gomez{-}Krämer and Olfa {Ben Ahmed} and Sylvain Marchand},
title={Towards Resource-Efficient Deep Learning for Train Scene Semantic Segmentation},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={347-354},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013139000003912},
isbn={978-989-758-728-3},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Towards Resource-Efficient Deep Learning for Train Scene Semantic Segmentation
SN - 978-989-758-728-3
IS - 2184-4321
AU - Iatrides, M.
AU - Gomez-Krämer, P.
AU - Ben Ahmed, O.
AU - Marchand, S.
PY - 2025
SP - 347
EP - 354
DO - 10.5220/0013139000003912
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic