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.
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Notes
- 1.
DIST_POFESR_PAUN_Ricerca Progetto “Rete Intelligente dei Parchi Archeologici” (RIPA -PAUN).
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Acknowledgments
ARGO drone was funded by Distretto ad alta tecnologia per i beni culturali DATABENC, PON 03PE_00164 “Rete Intelligente dei Parchi Archeologici (RIPA - PAUN)”. The authors sincerely thanks Gallenoplastica Srl for the active collaboration in the hull construction. This paper also benefited from the discussion(s) at the Neptune meeting (INQUA CMP project 2003P).
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Ciaramella, A., Perrotta, F., Pappone, G., Aucelli, P., Peluso, F., Mattei, G. (2021). Environment Object Detection for Marine ARGO Drone by Deep Learning. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12666. Springer, Cham. https://doi.org/10.1007/978-3-030-68780-9_12
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