Developing the Wheel Image Similarity Application with Deep Metric Learning: Hyundai Motor Company Case

Kyung Pyo Kang; Ga Hyeon Jeong; Jeong Hoon Eom; Soon Beom Kwon; Jae Hong Park

doi:10.1609/aaai.v37i13.26839

Developing the Wheel Image Similarity Application with Deep Metric Learning: Hyundai Motor Company Case

Kyung Pyo Kang, Ga Hyeon Jeong, Jeong Hoon Eom, Soon Beom Kwon, Jae Hong Park

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

Abstract

The global automobile market experiences quick changes in design preferences. In response to the demand shifts, manufacturers now try to apply new technologies to bring a novel design to market faster. In this paper, we introduce a novel application that performs a similarity verification task of wheel designs using an AI model and cloud computing technology. At Jan 2022, we successfully implemented the application to the wheel design process of Hyundai Motor Company's design team and shortened the similarity verification time by 90% to a maximum of 10 minutes. We believe that this study is the first to build a wheel image database and empirically prove that the cross-entropy loss does similar tasks as the pairwise losses do in the embedding space. As a result, we successfully automated Hyundai Motor's verification task of wheel design similarity. With a few clicks, the end-users in Hyundai Motor could take advantage of our application.

Original language	English
Title of host publication	AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations
Editors	Brian Williams, Yiling Chen, Jennifer Neville
Publisher	AAAI Press
Pages	15512-15518
Number of pages	7
ISBN (Electronic)	9781577358800
DOIs	https://doi.org/10.1609/aaai.v37i13.26839
Publication status	Published - 27 Jun 2023
Event	37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States Duration: 7 Feb 2023 → 14 Feb 2023

Publication series

Name	Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
Volume	37

Conference

Conference	37th AAAI Conference on Artificial Intelligence, AAAI 2023
Country/Territory	United States
City	Washington
Period	7/02/23 → 14/02/23

Bibliographical note

Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Access to Document

10.1609/aaai.v37i13.26839

Cite this

Kang, K. P., Jeong, G. H., Eom, J. H., Kwon, S. B., & Park, J. H. (2023). Developing the Wheel Image Similarity Application with Deep Metric Learning: Hyundai Motor Company Case. In B. Williams, Y. Chen, & J. Neville (Eds.), AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations (pp. 15512-15518). (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; Vol. 37). AAAI Press. https://doi.org/10.1609/aaai.v37i13.26839

Kang, Kyung Pyo ; Jeong, Ga Hyeon ; Eom, Jeong Hoon et al. / Developing the Wheel Image Similarity Application with Deep Metric Learning : Hyundai Motor Company Case. AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations. editor / Brian Williams ; Yiling Chen ; Jennifer Neville. AAAI Press, 2023. pp. 15512-15518 (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023).

@inproceedings{261a26d3fce84719918a6b0088146b55,

title = "Developing the Wheel Image Similarity Application with Deep Metric Learning: Hyundai Motor Company Case",

abstract = "The global automobile market experiences quick changes in design preferences. In response to the demand shifts, manufacturers now try to apply new technologies to bring a novel design to market faster. In this paper, we introduce a novel application that performs a similarity verification task of wheel designs using an AI model and cloud computing technology. At Jan 2022, we successfully implemented the application to the wheel design process of Hyundai Motor Company's design team and shortened the similarity verification time by 90% to a maximum of 10 minutes. We believe that this study is the first to build a wheel image database and empirically prove that the cross-entropy loss does similar tasks as the pairwise losses do in the embedding space. As a result, we successfully automated Hyundai Motor's verification task of wheel design similarity. With a few clicks, the end-users in Hyundai Motor could take advantage of our application.",

author = "Kang, {Kyung Pyo} and Jeong, {Ga Hyeon} and Eom, {Jeong Hoon} and Kwon, {Soon Beom} and Park, {Jae Hong}",

note = "Publisher Copyright: Copyright {\textcopyright} 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 37th AAAI Conference on Artificial Intelligence, AAAI 2023 ; Conference date: 07-02-2023 Through 14-02-2023",

year = "2023",

month = jun,

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doi = "10.1609/aaai.v37i13.26839",

language = "English",

series = "Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023",

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Kang, KP, Jeong, GH, Eom, JH, Kwon, SB & Park, JH 2023, Developing the Wheel Image Similarity Application with Deep Metric Learning: Hyundai Motor Company Case. in B Williams, Y Chen & J Neville (eds), AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations. Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, vol. 37, AAAI Press, pp. 15512-15518, 37th AAAI Conference on Artificial Intelligence, AAAI 2023, Washington, United States, 7/02/23. https://doi.org/10.1609/aaai.v37i13.26839

Developing the Wheel Image Similarity Application with Deep Metric Learning: Hyundai Motor Company Case. / Kang, Kyung Pyo; Jeong, Ga Hyeon; Eom, Jeong Hoon et al.
AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations. ed. / Brian Williams; Yiling Chen; Jennifer Neville. AAAI Press, 2023. p. 15512-15518 (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; Vol. 37).

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

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AB - The global automobile market experiences quick changes in design preferences. In response to the demand shifts, manufacturers now try to apply new technologies to bring a novel design to market faster. In this paper, we introduce a novel application that performs a similarity verification task of wheel designs using an AI model and cloud computing technology. At Jan 2022, we successfully implemented the application to the wheel design process of Hyundai Motor Company's design team and shortened the similarity verification time by 90% to a maximum of 10 minutes. We believe that this study is the first to build a wheel image database and empirically prove that the cross-entropy loss does similar tasks as the pairwise losses do in the embedding space. As a result, we successfully automated Hyundai Motor's verification task of wheel design similarity. With a few clicks, the end-users in Hyundai Motor could take advantage of our application.

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Kang KP, Jeong GH, Eom JH, Kwon SB, Park JH. Developing the Wheel Image Similarity Application with Deep Metric Learning: Hyundai Motor Company Case. In Williams B, Chen Y, Neville J, editors, AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations. AAAI Press. 2023. p. 15512-15518. (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023). doi: 10.1609/aaai.v37i13.26839

Developing the Wheel Image Similarity Application with Deep Metric Learning: Hyundai Motor Company Case

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