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

Takashi Tanaka; Takeshi Shinoda

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

Takashi Tanaka, Takeshi Shinoda

Marine Systems Design

Research output: Contribution to conference › Paper › peer-review

2 Citations (Scopus)

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 language	English
Publication status	Published - 2018
Event	2018 SNAME Maritime Convention, SMC 2018 - Providence, United States Duration: Oct 24 2018 → Oct 27 2018

Conference

Conference	2018 SNAME Maritime Convention, SMC 2018
Country/Territory	United States
City	Providence
Period	10/24/18 → 10/27/18

All Science Journal Classification (ASJC) codes

General Engineering

Cite this

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title = "A method for extracting the work status in shipyard using deep neural networks",

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.",

author = "Takashi Tanaka and Takeshi Shinoda",

note = "Publisher Copyright: {\textcopyright} 2018 The Society of Naval Architects and Marine Engineers. All Rights Reserved.; 2018 SNAME Maritime Convention, SMC 2018 ; Conference date: 24-10-2018 Through 27-10-2018",

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TY - CONF

T1 - A method for extracting the work status in shipyard using deep neural networks

AU - Tanaka, Takashi

AU - Shinoda, Takeshi

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

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M3 - Paper

AN - SCOPUS:85059364944

T2 - 2018 SNAME Maritime Convention, SMC 2018

Y2 - 24 October 2018 through 27 October 2018

ER -

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

Abstract

Conference

All Science Journal Classification (ASJC) codes

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