Exploring Deep Learning Capabilities for Coastal Image Segmentation on Edge Devices

Jonay Suárez-Ramírez, Alejandro Betancor-Del-Rosario, Daniel Santana-Cedrés, Nelson Monzón, Nelson Monzón

2023

Abstract

Artificial Intelligence (AI) has become a revolutionary tool in multiple fields in the last decade. The appearance of hardware with improved capabilities has paved the way to apply image processing based on Deep Neural Networks to more complex tasks with lower costs. Nevertheless, some environments, such as remote areas, require the use of edge devices. Consequently, the algorithms must be suited to platforms with more constrained resources. This is crucial in the development of AI systems in seaside zones. In our work, we compare a wide range of recent state-of-the-art Deep Learning models for Semantic Segmentation over edge devices. Such segmentation techniques provide a better scene understanding, in particular in complex areas, providing pixel-level detection and classification. In this regard, coastal environments represent a clear example, where more specific tasks can be performed from these approaches, such as littering detection, surveillance, and shoreline changes, among many others.

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Paper Citation


in Harvard Style

Suárez-Ramírez J., Betancor-Del-Rosario A., Santana-Cedrés D. and Monzón N. (2023). Exploring Deep Learning Capabilities for Coastal Image Segmentation on Edge Devices. 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, SciTePress, pages 409-418. DOI: 10.5220/0011615400003417


in Bibtex Style

@conference{visapp23,
author={Jonay Suárez-Ramírez and Alejandro Betancor-Del-Rosario and Daniel Santana-Cedrés and Nelson Monzón},
title={Exploring Deep Learning Capabilities for Coastal Image Segmentation on Edge Devices},
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={409-418},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011615400003417},
isbn={978-989-758-634-7},
}


in EndNote Style

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 - Exploring Deep Learning Capabilities for Coastal Image Segmentation on Edge Devices
SN - 978-989-758-634-7
AU - Suárez-Ramírez J.
AU - Betancor-Del-Rosario A.
AU - Santana-Cedrés D.
AU - Monzón N.
PY - 2023
SP - 409
EP - 418
DO - 10.5220/0011615400003417
PB - SciTePress