Abstract
Maritime operations vary greatly in character and requirements, ranging from shipping operations to search and rescue and safety operations. Maritime operations rely heavily on surveillance and require reliable and timely data that can inform decisions and planning. Critical information in such cases includes the exact location of objects in the water, such as vessels, persons and others. Due to the unique characteristics of the maritime environment the location of even inert objects changes through time, depending on weather conditions, water currents etc. Unmanned aerial vehicles (UAV) can be used to support maritime operations by providing live video streams and images from the area of operations. Machine learning algorithms can be developed, trained and used to automatically detect and track objects of specific types and characteristics. Within the context of the EFFECTOR project we developed and present here an embedded system that employs machine learning algorithms, allowing a UAV to autonomously detect objects in the water and keep track of their changing position through time. The system is meant to supplement search and rescue, as well as maritime safety operations where a report of an object in the water needs to be verified with the object detected and tracked, providing a live video stream to support decision making.
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Funding
This work is a part of the EFFECTOR project, funded by the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 883374. Content reflects only the authors’ view.
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Vasilopoulos, E., Vosinakis, G., Krommyda, M., Karagiannidis, L., Ouzounoglou, E., Amditis, A. (2022). Autonomous Object Detection Using a UAV Platform in the Maritime Environment. In: Guizzardi, R., Ralyté, J., Franch, X. (eds) Research Challenges in Information Science. RCIS 2022. Lecture Notes in Business Information Processing, vol 446. Springer, Cham. https://doi.org/10.1007/978-3-031-05760-1_33
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