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CTIM, Instituto Universitario de Cibernética, Empresas y Sociedad, University of Las Palmas de Gran Canaria, Spain

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2 Papers

Exploring Deep Learning Capabilities for Coastal Image Segmentation on Edge Devices

Topics: Machine Learning Technologies for Vision; Camera Networks and Vision; Video Surveillance and Event Detection; Deep Learning for Visual Understanding

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

Digital Object Identifier: 10.5220/0011615400003417

Proceedings of VISAPP 2023/VISIGRAPP, ISBN: 978-989-758-634-7

Quick 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.

Maritime Surveillance by Multiple Data Fusion: An Application Based on Deep Learning Object Detection, AIS Data and Geofencing

Topics: Camera Networks and Vision; Object Detection and Localization; Video Surveillance and Event Detection; Human and Computer Interaction; Deep Learning for Visual Understanding ; Deep Learning for Tracking

Authors: Sergio Ballines-Barrera ¹ , Leopoldo López ¹ , 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

Digital Object Identifier: 10.5220/0011670100003417

Proceedings of VISAPP 2023/VISIGRAPP, ISBN: 978-989-758-634-7

Quick Abstract

Marine traffic represents one of the critical points in coastal monitoring. This task has been eased by the development of Automatic Identification Systems (AIS), which allow ship recognition. However, AIS technology is not mandatory for all vessels, so there is a need for using alternative techniques to identify and track them. In this paper, we present the integration of several technologies. First, we perform ship detection by using different camera-based approaches, depending on the moment of the day (daytime or nighttime). From this detection, we estimate the vessel’s georeferenced position. Secondly, this estimation is combined with the information provided by AIS devices. We obtain a correspondence between the scene and the AIS data and we also detect ships without VHF transmitters. Together with a geofencing technique, we introduce a solution that fuses data from different sources, providing useful information for decision-making regarding the presence of vessels in near-shore locations.