An automated work observation method for shipyards using deep neural networks

研究成果: Contribution to conferencePaper査読

抄録

It is important to increase the productivity of every shipyard. Visualizing the actual work status during any industrial activity is essential. Work observation as one of the methods of industrial engineering has been applied in various fields in shipyards in Japan to increase productivity. However, current work observation requires both time and labor, and in some cases, shipyards hesitate to implement work observation. The aim of this study was to develop a methodology that uses deep neural networks to reduce the disadvantages of current work observation approaches while identifying work tasks and the accuracy of this observation.

本文言語英語
出版ステータス出版済み - 1 1 2019
イベントSNAME Maritime Convention 2019, SMC 2019 - Tacoma, 米国
継続期間: 10 30 201911 1 2019

会議

会議SNAME Maritime Convention 2019, SMC 2019
国/地域米国
CityTacoma
Period10/30/1911/1/19

All Science Journal Classification (ASJC) codes

  • 水圏科学
  • 管理、モニタリング、政策と法律
  • 水の科学と技術
  • 開発
  • 地理、計画および開発

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