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).
References
Ascione, A., et al.: Geomorphology of Naples and the Campi Flegrei: human and natural landscapes in a restless land. J. Maps, 1–11 (2020)
Aucelli, P., Cinque, A., Mattei, G., Pappone, G.: Historical sea level changes and effects on the coasts of Sorrento Peninsula (Gulf of Naples): New constrains from recent geoarchaeological investigations. Palaeogeogr. Palaeoclimatol. Palaeoecol. 463, 112–125 (2016a)
Aucelli, P., Cinque, A., Giordano, F., Mattei, G.: A geoarchaeological survey of the marine extension of the Roman archaeological site Villa del Pezzolo, Vico Equense, on the Sorrento Peninsula. Italy. Geoarchaeology 31(3), 244–252 (2016b)
Aucelli, P.P.C., Cinque, A., Mattei, G., Pappone, G.: Late Holocene landscape evolution of the gulf of Naples (Italy) inferred from geoarchaeological data. J. Maps 13(2), 300–310 (2017)
Aucelli, P., Cinque, A., Mattei, G., Pappone, G., Stefanile, M.: Coastal landscape evolution of Naples (Southern Italy) since the Roman period from archaeological and geomorphological data at Palazzo degli Spiriti site. Qua. Int. 483, 23–38 (2018a). https://doi.org/10.1016/j.quaint.2017.12.040
Aucelli, P.P., Cinque, A., Mattei, G., Pappone, G., Stefanile, M.: First results on the coastal changes related to local sea level variations along the Puteoli sector (Campi Flegrei, Italy) during the historical times. Alp. Mediterr. Quat. 31, 13–16 (2018b)
Aucelli, P., Cinque, A., Mattei, G., Pappone, G., Rizzo, A.: Studying relative sea level change and correlative adaptation of coastal structures on submerged Roman time ruins nearby Naples (southern Italy). Quat. Int. 501, 328–348 (2019). https://doi.org/10.1016/j.quaint.2017.10.011
Aucelli, P.P.C., et al.: Ancient Coastal Changes Due to Ground Movements and Human Interventions in the Roman Portus Julius (Pozzuoli Gulf, Italy): Results from Photogrammetric and Direct Surveys. Water 12(3), 658 (2020). https://doi.org/10.3390/w12030658
Ciaramella, A., Nardone, D., Staiano, A.: Data integration by fuzzy similarity-based hierarchical clustering. BMC Bioinform. 21, 350 (2020)
Ciaramella, A., Gianfico, M., Giunta, G.: Compressive sampling and adaptive dictionary learning for the packet loss recovery in audio multimedia streaming. Multimed. Tools Appl. 75(24), 17375–17392 (2016)
Ciaramella, A., De Lauro, E., De Martino, S., Di Lieto, B., Falanga, M., Tagliaferri, R.: Characterization of Strombolian events by using independent component analysis. Nonlin. Process. Geophys. 11(4), 453–461 (2004)
Giordano, F., Mattei, G., Parente, C., Peluso, F., Santamaria, R.: Integrating sensors into a marine drone for bathymetric 3D surveys in shallow waters. Sensors (MDPI) 16(1), 41 (2016). https://doi.org/10.3390/s16010041
Mattei, G., Troisi, S., Aucelli, P., Pappone, G., Peluso, F., Stefanile, M.: Sensing the Submerged Landscape of Nisida Roman Harbour in the Gulf of Naples from Integrated Measurements on a USV. Water 10(11), 1686 (2018a)
Mattei, G., Troisi, S., Aucelli, P.P., Pappone, G., Peluso, F., Stefanile, M.: Multiscale reconstruction of natural and archaeological underwater landscape by optical and acoustic sensors. In: 2018 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea), pp. 46–49. IEEE (2018b)
Mattei, G., Rizzo, A., Anfuso, G., Aucelli, P.P.C., Gracia, F.J.: A tool for evaluating the archaeological heritage vulnerability to coastal processes: the case study of Naples Gulf (southern Italy). Ocean Coastal Manage. 179, 104876 (2019)
Mattei, G., Aucelli, P.P., Caporizzo, C., Rizzo, A., Pappone, G.: New geomorphological and historical elements on morpho-evolutive trends and relative sea-level changes of Naples coast in the last 6000 years. Water 12(9), 2651 (2020)
Pappone, G., Aucelli, P.P., Mattei, G., Peluso, F., Stefanile, M., Carola, A.: A detailed reconstruction of the roman Landscape and the submerged archaeological structure at “Castel dell’Ovo islet" (Naples, Southern Italy). Geosciences 9(4), 170 (2019)
Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, pp. 1440–1448 (2015). https://doi.org/10.1109/ICCV.2015.169
Howard, A.G.: MobileNets: efficient convolutional neural networks for mobile vision applications. CoRR abs/1704.04861 (2017)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Ren, S., He, K., Girshick, R.: Sun. Towards real-time object detection with region proposal networks, NIPS, Faster R-CNN (2015)
Szegedy, C.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
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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