An automated work observation method for shipyards using deep neural networks

Takeshi Shinoda, Takashi Tanaka, Hayato Okamoto

Research output: Contribution to conferencePaperpeer-review


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.

Original languageEnglish
Publication statusPublished - Jan 1 2019
EventSNAME Maritime Convention 2019, SMC 2019 - Tacoma, United States
Duration: Oct 30 2019Nov 1 2019


ConferenceSNAME Maritime Convention 2019, SMC 2019
Country/TerritoryUnited States

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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