A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Deep Learning Test Platform for Maritime Applications: Development of the eM/S Salama Unmanned Surface Vessel and Its Remote Operations Center for Sensor Data Collection and Algorithm Development
Tekijät: Kalliovaara, Juha; Jokela, Tero; Asadi, Mehdi; Majd, Amin; Hallio, Juhani; Auranen, Jani; Seppanen, Mika; Putkonen, Ari; Koskinen, Juho; Tuomola, Tommi; Mohammadi Moghaddam, Reza; Paavola, Jarkko
Kustantaja: MDPI
Kustannuspaikka: BASEL
Julkaisuvuosi: 2024
Journal: Remote Sensing
Tietokannassa oleva lehden nimi: REMOTE SENSING
Lehden akronyymi: REMOTE SENS-BASEL
Artikkelin numero: 1545
Vuosikerta: 16
Numero: 9
Sivujen määrä: 35
eISSN: 2072-4292
DOI: https://doi.org/10.3390/rs16091545
Verkko-osoite: https://doi.org/10.3390/rs16091545
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/457232565
In response to the global megatrends of digitalization and transportation automation, Turku University of Applied Sciences has developed a test platform to advance autonomous maritime operations. This platform includes the unmanned surface vessel eM/S Salama and a remote operations center, both of which are detailed in this article. The article highlights the importance of collecting and annotating multi-modal sensor data from the vessel. These data are vital for developing deep learning algorithms that enhance situational awareness and guide autonomous navigation. By securing relevant data from maritime environments, we aim to enhance the autonomous features of unmanned surface vessels using deep learning techniques. The annotated sensor data will be made available for further research through open access. An image dataset, which includes synthetically generated weather conditions, is published alongside this article. While existing maritime datasets predominantly rely on RGB cameras, our work underscores the need for multi-modal data to advance autonomous capabilities in maritime applications.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
This research was co-funded by European Regional Development Fund, with the REACT EU support for regions to recover from the coronavirus pandemic, with grant numbers A78624 and A80845. This research was co-funded by the Finnish Ministry of Education and Culture in Applied Research Platform for Autonomous Systems (ARPA) project, 2020-2023. This research was co-funded by Business Finland in 5G-Advanced for Digitalization of Maritime Operations (ADMO) project.
