Artirilmiş gerçeklik gözlükleri ile nesne tanima

Mehmet Selcuk Albayrak; Alper O. Ner; Hazim Kemal Ekenel

doi:10.1109/SIU.2019.8806255

Artirilmiş gerçeklik gözlükleri ile nesne tanima

Translated title of the contribution: Object recognition on augmented reality glasses

Mehmet Selcuk Albayrak, Alper O. Ner, Hazim Kemal Ekenel

Department of Computer Engineering

Innovation and Transformation Department

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Employee training in fast-food restaurants is a long, practice-based process which is mainly done on the job. Employee performance during training directly affects service quality and customer satisfaction. In this study, it is aimed to optimize the training process in fast-food restaurants with use of augmented reality glasses. For this purpose, a comparative study is performed to determine the most suitable model of augmented reality glasses in the market. It is aimed to shorten the training period of the employee, to help the employee in this process and to introduce the objects around him. Light convolutional neural networks are compared to solve the object recognition problem on augmented reality glasses. As a result, MobileNet model is selected and fine-tuned to recognize the objects in a restaurant kitchen. The outcomes of this study will be used to fully train and supervise the employees without the need for a trainer in the future.

Translated title of the contribution	Object recognition on augmented reality glasses
Original language	Turkish
Title of host publication	27th Signal Processing and Communications Applications Conference, SIU 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728119045
DOIs	https://doi.org/10.1109/SIU.2019.8806255
Publication status	Published - Apr 2019
Event	27th Signal Processing and Communications Applications Conference, SIU 2019 - Sivas, Turkey Duration: 24 Apr 2019 → 26 Apr 2019

Publication series

Name	27th Signal Processing and Communications Applications Conference, SIU 2019

Conference

Conference	27th Signal Processing and Communications Applications Conference, SIU 2019
Country/Territory	Turkey
City	Sivas
Period	24/04/19 → 26/04/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Access to Document

10.1109/SIU.2019.8806255

Cite this

Albayrak, M. S., Ner, A. O., & Ekenel, H. K. (2019). Artirilmiş gerçeklik gözlükleri ile nesne tanima. In 27th Signal Processing and Communications Applications Conference, SIU 2019 Article 8806255 (27th Signal Processing and Communications Applications Conference, SIU 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2019.8806255

@inproceedings{6b0ba5f2da314edc9255d975d8d3fb69,

title = "Artirilmi{\c s} ger{\c c}eklik g{\"o}zl{\"u}kleri ile nesne tanima",

abstract = "Employee training in fast-food restaurants is a long, practice-based process which is mainly done on the job. Employee performance during training directly affects service quality and customer satisfaction. In this study, it is aimed to optimize the training process in fast-food restaurants with use of augmented reality glasses. For this purpose, a comparative study is performed to determine the most suitable model of augmented reality glasses in the market. It is aimed to shorten the training period of the employee, to help the employee in this process and to introduce the objects around him. Light convolutional neural networks are compared to solve the object recognition problem on augmented reality glasses. As a result, MobileNet model is selected and fine-tuned to recognize the objects in a restaurant kitchen. The outcomes of this study will be used to fully train and supervise the employees without the need for a trainer in the future.",

keywords = "Augmented reality glasses, Convolutional neural networks, Deep learning, Fast food employee training",

author = "Albayrak, {Mehmet Selcuk} and Ner, {Alper O.} and Ekenel, {Hazim Kemal}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 27th Signal Processing and Communications Applications Conference, SIU 2019 ; Conference date: 24-04-2019 Through 26-04-2019",

year = "2019",

month = apr,

doi = "10.1109/SIU.2019.8806255",

language = "T{\"u}rk{\c c}e",

series = "27th Signal Processing and Communications Applications Conference, SIU 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "27th Signal Processing and Communications Applications Conference, SIU 2019",

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}

Albayrak, MS, Ner, AO & Ekenel, HK 2019, Artirilmiş gerçeklik gözlükleri ile nesne tanima. in 27th Signal Processing and Communications Applications Conference, SIU 2019., 8806255, 27th Signal Processing and Communications Applications Conference, SIU 2019, Institute of Electrical and Electronics Engineers Inc., 27th Signal Processing and Communications Applications Conference, SIU 2019, Sivas, Turkey, 24/04/19. https://doi.org/10.1109/SIU.2019.8806255

Artirilmiş gerçeklik gözlükleri ile nesne tanima. / Albayrak, Mehmet Selcuk; Ner, Alper O.; Ekenel, Hazim Kemal.
27th Signal Processing and Communications Applications Conference, SIU 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8806255 (27th Signal Processing and Communications Applications Conference, SIU 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Artirilmiş gerçeklik gözlükleri ile nesne tanima

AU - Albayrak, Mehmet Selcuk

AU - Ner, Alper O.

AU - Ekenel, Hazim Kemal

PY - 2019/4

Y1 - 2019/4

N2 - Employee training in fast-food restaurants is a long, practice-based process which is mainly done on the job. Employee performance during training directly affects service quality and customer satisfaction. In this study, it is aimed to optimize the training process in fast-food restaurants with use of augmented reality glasses. For this purpose, a comparative study is performed to determine the most suitable model of augmented reality glasses in the market. It is aimed to shorten the training period of the employee, to help the employee in this process and to introduce the objects around him. Light convolutional neural networks are compared to solve the object recognition problem on augmented reality glasses. As a result, MobileNet model is selected and fine-tuned to recognize the objects in a restaurant kitchen. The outcomes of this study will be used to fully train and supervise the employees without the need for a trainer in the future.

AB - Employee training in fast-food restaurants is a long, practice-based process which is mainly done on the job. Employee performance during training directly affects service quality and customer satisfaction. In this study, it is aimed to optimize the training process in fast-food restaurants with use of augmented reality glasses. For this purpose, a comparative study is performed to determine the most suitable model of augmented reality glasses in the market. It is aimed to shorten the training period of the employee, to help the employee in this process and to introduce the objects around him. Light convolutional neural networks are compared to solve the object recognition problem on augmented reality glasses. As a result, MobileNet model is selected and fine-tuned to recognize the objects in a restaurant kitchen. The outcomes of this study will be used to fully train and supervise the employees without the need for a trainer in the future.

KW - Augmented reality glasses

KW - Convolutional neural networks

KW - Deep learning

KW - Fast food employee training

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U2 - 10.1109/SIU.2019.8806255

DO - 10.1109/SIU.2019.8806255

M3 - Konferans katkısı

AN - SCOPUS:85071994178

T3 - 27th Signal Processing and Communications Applications Conference, SIU 2019

BT - 27th Signal Processing and Communications Applications Conference, SIU 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 27th Signal Processing and Communications Applications Conference, SIU 2019

Y2 - 24 April 2019 through 26 April 2019

ER -

Artirilmiş gerçeklik gözlükleri ile nesne tanima

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