A method for extracting the work status in shipyard using deep neural networks

Research output: Contribution to conferencePaper

Abstract

It is necessary to increase the productivity in every shipyard. To visualize the actual work status in any industrial activity, the observation of the work being performed is essential. However, current work observation requires both time and labor and some cases, shipyards hesitate to implement work observation. The aim of this study is to develop a new work observation method by use of deep neural networks to reduce the disadvantages of current work observation approaches while improving the accuracy of work identification.

Original languageEnglish
Publication statusPublished - Jan 1 2018
Event2018 SNAME Maritime Convention, SMC 2018 - Providence, United States
Duration: Oct 24 2018Oct 27 2018

Conference

Conference2018 SNAME Maritime Convention, SMC 2018
CountryUnited States
CityProvidence
Period10/24/1810/27/18

Fingerprint

Shipyards
Productivity
Personnel
Deep neural networks

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Tanaka, T., & Shinoda, T. (2018). A method for extracting the work status in shipyard using deep neural networks. Paper presented at 2018 SNAME Maritime Convention, SMC 2018, Providence, United States.

A method for extracting the work status in shipyard using deep neural networks. / Tanaka, Takashi; Shinoda, Takeshi.

2018. Paper presented at 2018 SNAME Maritime Convention, SMC 2018, Providence, United States.

Research output: Contribution to conferencePaper

Tanaka, T & Shinoda, T 2018, 'A method for extracting the work status in shipyard using deep neural networks' Paper presented at 2018 SNAME Maritime Convention, SMC 2018, Providence, United States, 10/24/18 - 10/27/18, .
Tanaka T, Shinoda T. A method for extracting the work status in shipyard using deep neural networks. 2018. Paper presented at 2018 SNAME Maritime Convention, SMC 2018, Providence, United States.
Tanaka, Takashi ; Shinoda, Takeshi. / A method for extracting the work status in shipyard using deep neural networks. Paper presented at 2018 SNAME Maritime Convention, SMC 2018, Providence, United States.
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