Application of Convolutional Neural Network (CNN) to Recognize Ship Structures

The purpose of this paper is to study the recognition of ships and their structures to improve the safety of drone operations engaged in shore-to-ship drone delivery service. This study has developed a system that can distinguish between ships and their structures by using a convolutional neural net...

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Autores principales: Lim, Jae-Jun, Kim, Dae-Won, Hong, Woon-Hee, Kim, Min, Lee, Dong-Hoon, Kim, Sun-Young, Jeong, Jae-Hoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9145347/
https://www.ncbi.nlm.nih.gov/pubmed/35632233
http://dx.doi.org/10.3390/s22103824
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author Lim, Jae-Jun
Kim, Dae-Won
Hong, Woon-Hee
Kim, Min
Lee, Dong-Hoon
Kim, Sun-Young
Jeong, Jae-Hoon
author_facet Lim, Jae-Jun
Kim, Dae-Won
Hong, Woon-Hee
Kim, Min
Lee, Dong-Hoon
Kim, Sun-Young
Jeong, Jae-Hoon
author_sort Lim, Jae-Jun
collection PubMed
description The purpose of this paper is to study the recognition of ships and their structures to improve the safety of drone operations engaged in shore-to-ship drone delivery service. This study has developed a system that can distinguish between ships and their structures by using a convolutional neural network (CNN). First, the dataset of the Marine Traffic Management Net is described and CNN’s object sensing based on the Detectron2 platform is discussed. There will also be a description of the experiment and performance. In addition, this study has been conducted based on actual drone delivery operations—the first air delivery service by drones in Korea.
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spelling pubmed-91453472022-05-29 Application of Convolutional Neural Network (CNN) to Recognize Ship Structures Lim, Jae-Jun Kim, Dae-Won Hong, Woon-Hee Kim, Min Lee, Dong-Hoon Kim, Sun-Young Jeong, Jae-Hoon Sensors (Basel) Communication The purpose of this paper is to study the recognition of ships and their structures to improve the safety of drone operations engaged in shore-to-ship drone delivery service. This study has developed a system that can distinguish between ships and their structures by using a convolutional neural network (CNN). First, the dataset of the Marine Traffic Management Net is described and CNN’s object sensing based on the Detectron2 platform is discussed. There will also be a description of the experiment and performance. In addition, this study has been conducted based on actual drone delivery operations—the first air delivery service by drones in Korea. MDPI 2022-05-18 /pmc/articles/PMC9145347/ /pubmed/35632233 http://dx.doi.org/10.3390/s22103824 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Lim, Jae-Jun
Kim, Dae-Won
Hong, Woon-Hee
Kim, Min
Lee, Dong-Hoon
Kim, Sun-Young
Jeong, Jae-Hoon
Application of Convolutional Neural Network (CNN) to Recognize Ship Structures
title Application of Convolutional Neural Network (CNN) to Recognize Ship Structures
title_full Application of Convolutional Neural Network (CNN) to Recognize Ship Structures
title_fullStr Application of Convolutional Neural Network (CNN) to Recognize Ship Structures
title_full_unstemmed Application of Convolutional Neural Network (CNN) to Recognize Ship Structures
title_short Application of Convolutional Neural Network (CNN) to Recognize Ship Structures
title_sort application of convolutional neural network (cnn) to recognize ship structures
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9145347/
https://www.ncbi.nlm.nih.gov/pubmed/35632233
http://dx.doi.org/10.3390/s22103824
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