Aim of this work is to implement an environment object detection system for a marine drone. A Deep Learning based model for object detection is embedded on ARGO drone equipped with geophysical sensors and several on-board cameras. The marine drone, developed at iMTG laboratory in partnership with NEPTUN-IA laboratory, was designed to obtain high-resolution mapping of nearshore-to-foreshore sectors and equipped with a system able to detect and identify Ground Control Point (GCP) in real time. A Deep Neural Network is embedded on a Raspberry PI platform and it is adopted for developing the object detection module. Real experiments and comparisons are conducted for identifying GCP among the roughness and vegetation present in the seabed.

Environment Object Detection for Marine ARGO Drone by Deep Learning

Ciaramella A.;Perrotta F.;Pappone G.;Aucelli P.;Peluso F.
2021

Abstract

Aim of this work is to implement an environment object detection system for a marine drone. A Deep Learning based model for object detection is embedded on ARGO drone equipped with geophysical sensors and several on-board cameras. The marine drone, developed at iMTG laboratory in partnership with NEPTUN-IA laboratory, was designed to obtain high-resolution mapping of nearshore-to-foreshore sectors and equipped with a system able to detect and identify Ground Control Point (GCP) in real time. A Deep Neural Network is embedded on a Raspberry PI platform and it is adopted for developing the object detection module. Real experiments and comparisons are conducted for identifying GCP among the roughness and vegetation present in the seabed.
978-3-030-68779-3
978-3-030-68780-9
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/98919
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