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DC Field | Value | Language |
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dc.citation.number | 10 | - |
dc.citation.startPage | 3824 | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 22 | - |
dc.contributor.author | Lim, Jae-Jun | - |
dc.contributor.author | Kim, Dae-Won | - |
dc.contributor.author | Hong, Woon-Hee | - |
dc.contributor.author | Kim, Min | - |
dc.contributor.author | Lee, Dong-Hoon | - |
dc.contributor.author | Kim, Sun-Young | - |
dc.contributor.author | Jeong, Jae-Hoon | - |
dc.date.accessioned | 2023-12-21T14:11:07Z | - |
dc.date.available | 2023-12-21T14:11:07Z | - |
dc.date.created | 2022-06-16 | - |
dc.date.issued | 2022-05 | - |
dc.description.abstract | 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. | - |
dc.identifier.bibliographicCitation | SENSORS, v.22, no.10, pp.3824 | - |
dc.identifier.doi | 10.3390/s22103824 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/58689 | - |
dc.identifier.url | https://www.mdpi.com/1424-8220/22/10/3824 | - |
dc.identifier.wosid | 000801317700001 | - |
dc.language | 영어 | - |
dc.publisher | MDPI | - |
dc.title | Application of Convolutional Neural Network (CNN) to Recognize Ship Structures | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation | - |
dc.relation.journalResearchArea | Chemistry; Engineering; Instruments & Instrumentation | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.subject.keywordAuthor | convolutional neural network (CNN) | - |
dc.subject.keywordAuthor | recognize ship structures | - |
dc.subject.keywordAuthor | mask R-CNN | - |
dc.subject.keywordAuthor | faster R-CNN | - |
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