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

Sergio Ballines-Barrera, Leopoldo López, Daniel Santana-Cedrés, Nelson Monzón, Nelson Monzón

2023

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.

Paper Citation

in Harvard Style

Ballines-Barrera S., López L., Santana-Cedrés D. and Monzón N. (2023). Maritime Surveillance by Multiple Data Fusion: An Application Based on Deep Learning Object Detection, AIS Data and Geofencing. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 846-855. DOI: 10.5220/0011670100003417

in Bibtex Style

@conference{visapp23,
author={Sergio Ballines-Barrera and Leopoldo López and Daniel Santana-Cedrés and Nelson Monzón},
title={Maritime Surveillance by Multiple Data Fusion: An Application Based on Deep Learning Object Detection, AIS Data and Geofencing},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={846-855},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011670100003417},
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 4: VISAPP
TI - Maritime Surveillance by Multiple Data Fusion: An Application Based on Deep Learning Object Detection, AIS Data and Geofencing
SN - 978-989-758-634-7
AU - Ballines-Barrera S.
AU - López L.
AU - Santana-Cedrés D.
AU - Monzón N.
PY - 2023
SP - 846
EP - 855
DO - 10.5220/0011670100003417
PB - SciTePress