Marine litter is harmful to coastal and ocean environments for many reasons. Many devices have been developed in the last few years to recover as much litter as possible in ports and coastal areas. However, they usually employ a brute-force approach, leading to high energy and resource consumption. This is not negligible, considering their ecological goal. In this scenario, the deployment of Unmanned Surface Vehicles (USVs) to inspect the area of interest could complement the use of cleaning devices, adding relevant knowledge and intelligence to the context. Cleaning devices could be deployed or activated only where and when needed, leading to a more resource-aware approach. The contribution of this work is the development and deployment of an object detection neural network onto the H20mni-X USV, aiming to real-time detection of floating marine litter through an optical camera.
Real-Time Floating Marine Litter Detection on USV / Lazzerini, Guido; Topini, Alberto; Liverani, Gherardo; Cecchi, Lorenzo; Ridolfi, Alessandro; Ferreira, Fausto. - ELETTRONICO. - (2024), pp. 148-153. (Intervento presentato al convegno 2024 IEEE International Workshop on Metrology for the Sea, MetroSea 2024 tenutosi a Portorose, Slovenia nel 14-16 October 2024) [10.1109/metrosea62823.2024.10765677].
Real-Time Floating Marine Litter Detection on USV
Lazzerini, Guido
;Topini, Alberto;Liverani, Gherardo;Cecchi, Lorenzo;Ridolfi, Alessandro;
2024
Abstract
Marine litter is harmful to coastal and ocean environments for many reasons. Many devices have been developed in the last few years to recover as much litter as possible in ports and coastal areas. However, they usually employ a brute-force approach, leading to high energy and resource consumption. This is not negligible, considering their ecological goal. In this scenario, the deployment of Unmanned Surface Vehicles (USVs) to inspect the area of interest could complement the use of cleaning devices, adding relevant knowledge and intelligence to the context. Cleaning devices could be deployed or activated only where and when needed, leading to a more resource-aware approach. The contribution of this work is the development and deployment of an object detection neural network onto the H20mni-X USV, aiming to real-time detection of floating marine litter through an optical camera.File | Dimensione | Formato | |
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