Authors:
Jonay Suárez-Ramírez
1
;
Alejandro Betancor-Del-Rosario
2
;
Daniel Santana-Cedrés
2
and
Nelson Monzón
1
;
2
Affiliations:
1
Qualitas Artificial Intelligence and Science, Spain
;
2
CTIM, Instituto Universitario de Cibernética, Empresas y Sociedad, University of Las Palmas de Gran Canaria, Spain
Keyword(s):
Computer Vision, Deep Learning, Semantic Segmentation, Seaside Scenes, Edge Devices.
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 ma
ny others.
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