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

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 unt...

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Bibliographic Details
Published in:Marine Technology Society journal 2021-07, Vol.55 (4), p.88-98
Main Authors: Pereira, Maria Inês, Leite, Pedro Nuno, Maykol Pinto, Andry
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Autonomous Surface Vehicle
Autonomous surface vehicles
Docking
Harbors
Inertial platforms
Lidar
Machine Learning
Neural networks
Object Recognition
Random noise
Structures
Surface vehicles
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Summary: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.
ISSN:0025-3324
1948-1209
DOI:10.4031/MTSJ.55.4.9