Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/121014
Título: Exploring Deep Learning Capabilities for Coastal Image Segmentation on Edge Devices
Autores/as: Suárez Ramírez, Jonay 
Betancor-Del-Rosario, Alejandro
Santana Cedrés, Daniel 
Monzón, Nelson 
Clasificación UNESCO: 3304 Tecnología de los ordenadores
Palabras clave: Computer Vision
Deep Learning
Semantic Segmentation
Seaside Scenes
Edge Devices
Fecha de publicación: 2023
Editor/a: SciTePress Digital Library 
Proyectos: "A la ULPGC para análisis matemático de imágenes por CTIM" 
Publicación seriada: Proceedings Of The International Joint Conference On Computer Vision, Imaging And Computer Graphics Theory And Applications
Conferencia: 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) 
Resumen: 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.
URI: http://hdl.handle.net/10553/121014
ISBN: 978-989-758-634-7
ISSN: 2184-4321
DOI: 10.5220/0011615400003417
Fuente: Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5, p. 409-418
Colección:Actas de congresos
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