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Publications

2021

A 3-D Lightweight Convolutional Neural Network for Detecting Docking Structures in Cluttered Environments

Authors
Pereira, MI; Leite, PN; Pinto, AM;

Publication
MARINE TECHNOLOGY SOCIETY JOURNAL

Abstract
The maritime industry has been following the paradigm shift toward the automation of typically intelligent procedures, with research regarding autonomous surface vehicles (ASVs) having seen an upward trend in recent years. However, this type of vehicle cannot be employed on a full scale until a few challenges are solved. For example, the docking process of an ASV is still a demanding task that currently requires human intervention. This research work proposes a volumetric convolutional neural network (vCNN) for the detection of docking structures from 3-D data, developed according to a balance between precision and speed. Another contribution of this article is a set of synthetically generated data regarding the context of docking structures. The dataset is composed of LiDAR point clouds, stereo images, GPS, and Inertial Measurement Unit (IMU) information. Several robustness tests carried out with different levels of Gaussian noise demonstrated an average accuracy of 93.34% and a deviation of 5.46% for the worst case. Furthermore, the system was fine-tuned and evaluated in a real commercial harbor, achieving an accuracy of over 96%. The developed classifier is able to detect different types of structures and works faster than other state-of-the-art methods that establish their performance in real environments.

2021

Advancing Autonomous Surface Vehicles: A 3D Perception System for the Recognition and Assessment of Docking-Based Structures

Authors
Pereira, MI; Claro, RM; Leite, PN; Pinto, AM;

Publication
IEEE ACCESS

Abstract
The automation of typically intelligent and decision-making processes in the maritime industry leads to fewer accidents and more cost-effective operations. However, there are still lots of challenges to solve until fully autonomous systems can be employed. Artificial Intelligence (AI) has played a major role in this paradigm shift and shows great potential for solving some of these challenges, such as the docking process of an autonomous vessel. This work proposes a lightweight volumetric Convolutional Neural Network (vCNN) capable of recognizing different docking-based structures using 3D data in real-time. A synthetic-to-real domain adaptation approach is also proposed to accelerate the training process of the vCNN. This approach makes it possible to greatly decrease the cost of data acquisition and the need for advanced computational resources. Extensive experiments demonstrate an accuracy of over 90% in the recognition of different docking structures, using low resolution sensors. The inference time of the system was about 120ms on average. Results obtained using a real Autonomous Surface Vehicle (ASV) demonstrated that the vCNN trained with the synthetic-to-real domain adaptation approach is suitable for maritime mobile robots. This novel AI recognition method, combined with the utilization of 3D data, contributes to an increased robustness of the docking process regarding environmental constraints, such as rain and fog, as well as insufficient lighting in nighttime operations.

2021

Multiple Vessel Detection and Tracking in Harsh Maritime Environments

Authors
Duarte D.F.; Pereira M.I.; Pinto A.M.;

Publication
Oceans Conference Record (IEEE)

Abstract
Recently, research concerning the navigation of Autonomous Surface Vehicles (ASVs) has been increasing. However, a big scale implementation of these vessels is still held back by a plethora of challenges such as multi-object tracking. This article presents the development of a tracking model through transfer learning techniques, based on referenced object trackers for urban scenarios. The work consisted in training a neural network through deep learning techniques, including data association and comparison of three different optimisers, Adadelta, Adam and SGD, determining the best hyper-parameters to maximise the training efficiency. The developed model achieved decent performance at tracking large vessels in the ocean, being successful even in harsh lighting conditions and lack of image focus.

2020

Detecting Docking-based Structures for Persistent ASVs using a Volumetric Neural Network

Authors
Pereira, MI; Leite, PN; Pinto, AM;

Publication
GLOBAL OCEANS 2020: SINGAPORE - U.S. GULF COAST

Abstract
In recent years, research concerning the operation of Autonomous Surface Vehicles (ASVs) has seen an upward trend, although the full-scale application of this type of vehicles still encounters diverse limitations. In particular, the docking and undocking processes of an ASV are tasks that currently require human intervention. Aiming to take one step further towards enabling a vessel to dock autonomously, this article presents a Deep Learning approach to detect a docking structure in the environment surrounding the vessel. The work also included the acquisition of a dataset composed of LiDAR scans and RGB images, along with IMU and GPS information, obtained in simulation. The developed network achieved an accuracy of 95.99%, being robust to several degrees of Gaussian noise, with an average accuracy of 9334% and a deviation of 5.46% for the worst case.