An automated work observation method for shipyards using deep neural networks

研究成果: Contribution to conferencePaper

抜粋

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.

元の言語英語
出版物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
米国
Tacoma
期間10/30/1911/1/19

All Science Journal Classification (ASJC) codes

  • Aquatic Science
  • Management, Monitoring, Policy and Law
  • Water Science and Technology
  • Development
  • Geography, Planning and Development

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  • これを引用

    Shinoda, T., Tanaka, T., & Okamoto, H. (2019). An automated work observation method for shipyards using deep neural networks. 論文発表場所 SNAME Maritime Convention 2019, SMC 2019, Tacoma, 米国.