Environment Object Detection for Marine ARGO Drone by Deep Learning

Ciaramella, Angelo; Perrotta, Francesco; Pappone, Gerardo; Aucelli, Pietro P. C.; Peluso, Francesco; Mattei, Gaia

doi:10.1007/978-3-030-68780-9_12

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…The iMTG (Innovative Marine Technology for Geoarchaeology) laboratory of the Parthenope University has been experimenting with the use of marine drones in the coastal area since 2015, producing several prototypes with which a considerable amount of scientific data and operational experience has been gathered [ 46 ]. In light of this background, ARGO-USV (Argo-Hellenic-USV, Attiki, Greece) has been designed and engineered in 2020 for carrying out integrated surveys of coastal areas.…”

Section: Methodsmentioning

confidence: 99%

Multi-Method Technics and Deep Neural Networks Tools on Board ARGO USV for the Geoarchaeological and Geomorphological Mapping of Coastal Areas: The Case of Puteoli Roman Harbour

Mattei,

Aucelli,

Ciaramella

et al. 2024

Sensors

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The ARGO-USV (Unmanned Surface Vehicle for ARchaeological GeO-application) is a technological project involving a marine drone aimed at devising an innovative methodology for marine geological and geomorphological investigations in shallow areas, usually considered critical areas to be investigated, with the help of traditional vessels. The methodological approach proposed in this paper has been implemented according to a multimodal mapping technique involving the simultaneous and integrated use of both optical and geoacoustic sensors. This approach has been enriched by tools based on artificial intelligence (AI), specifically intended to be installed onboard the ARGO-USV, aimed at the automatic recognition of submerged targets and the physical characterization of the seabed. This technological project is composed of a main command and control system and a series of dedicated sub-systems successfully tested in different operational scenarios. The ARGO drone is capable of acquiring and storing a considerable amount of georeferenced data during surveys lasting a few hours. The transmission of all acquired data in broadcasting allows the cooperation of a multidisciplinary team of specialists able to analyze specific datasets in real time. These features, together with the use of deep-learning-based modules and special attention to green-compliant construction phases, are the particular aspects that make ARGO-USV a modern and innovative project, aiming to improve the knowledge of wide coastal areas while minimizing the impact on these environments. As a proof-of-concept, we present the extensive mapping and characterization of the seabed from a geoarchaeological survey of the underwater Roman harbor of Puteoli in the Gulf of Naples (Italy), demonstrating that deep learning techniques can work synergistically with seabed mapping methods.

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Section: Methodsmentioning

confidence: 99%

Multi-Method Technics and Deep Neural Networks Tools on Board ARGO USV for the Geoarchaeological and Geomorphological Mapping of Coastal Areas: The Case of Puteoli Roman Harbour

Mattei,

Aucelli,

Ciaramella

et al. 2024

Sensors

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“…Here, C1 represents the channel size of the input feature X, and V represents the number of regions where the feature map X is divided, which is also the number of nodes in the graph structure. In Step 1 of Algorithm 1, the calculation formulas of any element of the probability matrix Q ∈ R H×W×c1 and the coding matrix Z ∈ R c1×V are shown in Equations ( 5) and (6). In order to achieve multiscale context long-range dependence, this study sets the value of V to 4 and 8, respectively.…”

Section: Meta-feature Extractormentioning

confidence: 99%

“…Object detection is one of the important tasks in the understanding of satellite images, and it plays an indispensable role in military decision making, urban management, environmental monitoring, disaster detection, public safety, etc. [5][6][7][8][9]. Moreover, the result obtained by object detection techniques can also be used to track moving objects, which is of great significance for analyzing the state and movement pattern of the objects [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Few-Shot Object Detection in Remote Sensing Imagery via Fuse Context Dependencies and Global Features

Wang

Sui

et al. 2023

Remote Sensing

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The rapid development of Earth observation technology has promoted the continuous accumulation of images in the field of remote sensing. However, a large number of remote sensing images still lack manual annotations of objects, which makes the strongly supervised deep learning object detection method not widely used, as it lacks generalization ability for unseen object categories. Considering the above problems, this study proposes a few-shot remote sensing image object detection method that integrates context dependencies and global features. The method can be used to fine-tune the model with a small number of sample annotations based on the model trained in the base class, as a way to enhance the detection capability of new object classes. The method proposed in this study consists of three main modules, namely, the meta-feature extractor (ME), reweighting module (RM), and feature fusion module (FFM). These three modules are respectively used to enhance the context dependencies of the query set features, improve the global features of the support set that contains annotations, and finally fuse the query set features and support set features. The baseline of the meta-feature extractor of the entire framework is based on the optimized YOLOv5 framework. The reweighting module of the support set feature extraction is based on a simple convolutional neural network (CNN) framework, and the foreground feature enhancement of the support sets was made in the preprocessing stage. This study achieved beneficial results in the two benchmark datasets NWPU VHR-10 and DIOR. Compared with the comparison methods, the proposed method achieved the best performance in the object detection of the base class and the novel class.

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Robust Human-Identifiable Markers for Absolute Relocalization of Underwater Robots in Marine Data Science Applications

Herrmann,

Reissmann,

Rothenbeck

et al. 2022

Image Analysis and Processing – ICIAP 2022

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Environment Object Detection for Marine ARGO Drone by Deep Learning

Cited by 5 publications

References 19 publications

Multi-Method Technics and Deep Neural Networks Tools on Board ARGO USV for the Geoarchaeological and Geomorphological Mapping of Coastal Areas: The Case of Puteoli Roman Harbour

Multi-Method Technics and Deep Neural Networks Tools on Board ARGO USV for the Geoarchaeological and Geomorphological Mapping of Coastal Areas: The Case of Puteoli Roman Harbour

Few-Shot Object Detection in Remote Sensing Imagery via Fuse Context Dependencies and Global Features

Robust Human-Identifiable Markers for Absolute Relocalization of Underwater Robots in Marine Data Science Applications

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