Small Object Detection Technology Using Multi-Modal Data Based on Deep Learning

Chi Won Park; Yuri Seo; Teh Jen Sun; Ga Won Lee; Eui Nam Huh

doi:10.1109/ICOIN56518.2023.10049014

Small Object Detection Technology Using Multi-Modal Data Based on Deep Learning

Chi Won Park, Yuri Seo, Teh Jen Sun, Ga Won Lee, Eui Nam Huh

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

2 Citations (Scopus)

Abstract

Recently, Amazon Web Services (AWS) announced a plan to develop a computer vision artificial intelligence service. The field of computer vision has been receiving constant attention like this, and object detection technology is a very important part in this field of computer vision. Although object detection technology is used in many computer vision fields, object detection technology has many limitations in small object detection and night object detection. Various noise factors degrade the image quality, and it is difficult to expect high accuracy when detecting small objects in this environment. In this paper, we develop a technology that can solve these problems by using multi-modal data. In addition, this object detection technology can be used in various fields by designing and developing a lightweight system that can work well in a low-resource environment.

Original language	English
Title of host publication	37th International Conference on Information Networking, ICOIN 2023
Publisher	IEEE Computer Society
Pages	420-422
Number of pages	3
ISBN (Electronic)	9781665462686
DOIs	https://doi.org/10.1109/ICOIN56518.2023.10049014
Publication status	Published - 2023
Event	37th International Conference on Information Networking, ICOIN 2023 - Bangkok, Thailand Duration: 11 Jan 2023 → 14 Jan 2023

Publication series

Name	International Conference on Information Networking
Volume	2023-January
ISSN (Print)	1976-7684

Conference

Conference	37th International Conference on Information Networking, ICOIN 2023
Country/Territory	Thailand
City	Bangkok
Period	11/01/23 → 14/01/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Low Resource
Multi-Modal Data
Object Detection

Access to Document

10.1109/ICOIN56518.2023.10049014

Cite this

Park, C. W., Seo, Y., Sun, T. J., Lee, G. W., & Huh, E. N. (2023). Small Object Detection Technology Using Multi-Modal Data Based on Deep Learning. In 37th International Conference on Information Networking, ICOIN 2023 (pp. 420-422). (International Conference on Information Networking; Vol. 2023-January). IEEE Computer Society. https://doi.org/10.1109/ICOIN56518.2023.10049014

@inproceedings{67f6fe580ff541b2a586cfb3f28b9631,

title = "Small Object Detection Technology Using Multi-Modal Data Based on Deep Learning",

abstract = "Recently, Amazon Web Services (AWS) announced a plan to develop a computer vision artificial intelligence service. The field of computer vision has been receiving constant attention like this, and object detection technology is a very important part in this field of computer vision. Although object detection technology is used in many computer vision fields, object detection technology has many limitations in small object detection and night object detection. Various noise factors degrade the image quality, and it is difficult to expect high accuracy when detecting small objects in this environment. In this paper, we develop a technology that can solve these problems by using multi-modal data. In addition, this object detection technology can be used in various fields by designing and developing a lightweight system that can work well in a low-resource environment.",

keywords = "Low Resource, Multi-Modal Data, Object Detection",

author = "Park, {Chi Won} and Yuri Seo and Sun, {Teh Jen} and Lee, {Ga Won} and Huh, {Eui Nam}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 37th International Conference on Information Networking, ICOIN 2023 ; Conference date: 11-01-2023 Through 14-01-2023",

year = "2023",

doi = "10.1109/ICOIN56518.2023.10049014",

language = "English",

series = "International Conference on Information Networking",

publisher = "IEEE Computer Society",

pages = "420--422",

booktitle = "37th International Conference on Information Networking, ICOIN 2023",

address = "United States",

}

Park, CW, Seo, Y, Sun, TJ, Lee, GW & Huh, EN 2023, Small Object Detection Technology Using Multi-Modal Data Based on Deep Learning. in 37th International Conference on Information Networking, ICOIN 2023. International Conference on Information Networking, vol. 2023-January, IEEE Computer Society, pp. 420-422, 37th International Conference on Information Networking, ICOIN 2023, Bangkok, Thailand, 11/01/23. https://doi.org/10.1109/ICOIN56518.2023.10049014

Small Object Detection Technology Using Multi-Modal Data Based on Deep Learning. / Park, Chi Won; Seo, Yuri; Sun, Teh Jen et al.
37th International Conference on Information Networking, ICOIN 2023. IEEE Computer Society, 2023. p. 420-422 (International Conference on Information Networking; Vol. 2023-January).

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

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T1 - Small Object Detection Technology Using Multi-Modal Data Based on Deep Learning

AU - Park, Chi Won

AU - Seo, Yuri

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AU - Lee, Ga Won

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Y1 - 2023

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AB - Recently, Amazon Web Services (AWS) announced a plan to develop a computer vision artificial intelligence service. The field of computer vision has been receiving constant attention like this, and object detection technology is a very important part in this field of computer vision. Although object detection technology is used in many computer vision fields, object detection technology has many limitations in small object detection and night object detection. Various noise factors degrade the image quality, and it is difficult to expect high accuracy when detecting small objects in this environment. In this paper, we develop a technology that can solve these problems by using multi-modal data. In addition, this object detection technology can be used in various fields by designing and developing a lightweight system that can work well in a low-resource environment.

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KW - Object Detection

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DO - 10.1109/ICOIN56518.2023.10049014

M3 - Conference contribution

AN - SCOPUS:85149177277

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SP - 420

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BT - 37th International Conference on Information Networking, ICOIN 2023

PB - IEEE Computer Society

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Y2 - 11 January 2023 through 14 January 2023

ER -

Small Object Detection Technology Using Multi-Modal Data Based on Deep Learning

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