Learning an object detector using zoomed object regions

Sung Jin Cho; Seung Wook Kim; Kwang Hyun Uhm; Hyong Keun Kook; Sung Jea Ko

doi:10.23919/ELINFOCOM.2019.8706381

Learning an object detector using zoomed object regions

Sung Jin Cho, Seung Wook Kim, Kwang Hyun Uhm, Hyong Keun Kook, Sung Jea Ko

School of Electrical Engineering

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

1 Citation (Scopus)

Abstract

The single shot multi-box detector (SSD) is one of the first real-time detectors, which uses a convolutional neural network (CNN) and achieves the state-of-the-art detection performance. However, owing to the semantic gap between each feature layer of CNN, the SSD has a room for improvement. In this paper, we propose a novel training scheme to enhance the performance of the SSD. In object detection, ground truth (GT) box is a bounding box enclosing an object boundary. To improve the semantic level of the feature map, we generate additional GT boxes by zooming in to and out from the original GT boxes. Experimental results show that the SSD trained with our scheme outperforms the original one on public dataset.

Original language	English
Title of host publication	ICEIC 2019 - International Conference on Electronics, Information, and Communication
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9788995004449
DOIs	https://doi.org/10.23919/ELINFOCOM.2019.8706381
Publication status	Published - 2019 May 3
Event	18th International Conference on Electronics, Information, and Communication, ICEIC 2019 - Auckland, New Zealand Duration: 2019 Jan 22 → 2019 Jan 25

Publication series

Name	ICEIC 2019 - International Conference on Electronics, Information, and Communication

Conference

Conference	18th International Conference on Electronics, Information, and Communication, ICEIC 2019
Country/Territory	New Zealand
City	Auckland
Period	19/1/22 → 19/1/25

Bibliographical note

Funding Information:
This work was supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program) (10082585, Development of deep learning-based open EV platform technology capable of autonomous driving) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea).

Publisher Copyright:
© 2019 Institute of Electronics and Information Engineers (IEIE).

Keywords

Computer vision
Neural network
Object detection

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.23919/ELINFOCOM.2019.8706381

Cite this

Cho, S. J., Kim, S. W., Uhm, K. H., Kook, H. K., & Ko, S. J. (2019). Learning an object detector using zoomed object regions. In ICEIC 2019 - International Conference on Electronics, Information, and Communication Article 8706381 (ICEIC 2019 - International Conference on Electronics, Information, and Communication). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ELINFOCOM.2019.8706381

Learning an object detector using zoomed object regions. / Cho, Sung Jin; Kim, Seung Wook; Uhm, Kwang Hyun et al.
ICEIC 2019 - International Conference on Electronics, Information, and Communication. Institute of Electrical and Electronics Engineers Inc., 2019. 8706381 (ICEIC 2019 - International Conference on Electronics, Information, and Communication).

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

Cho, SJ, Kim, SW, Uhm, KH, Kook, HK & Ko, SJ 2019, Learning an object detector using zoomed object regions. in ICEIC 2019 - International Conference on Electronics, Information, and Communication., 8706381, ICEIC 2019 - International Conference on Electronics, Information, and Communication, Institute of Electrical and Electronics Engineers Inc., 18th International Conference on Electronics, Information, and Communication, ICEIC 2019, Auckland, New Zealand, 19/1/22. https://doi.org/10.23919/ELINFOCOM.2019.8706381

Cho SJ, Kim SW, Uhm KH, Kook HK, Ko SJ. Learning an object detector using zoomed object regions. In ICEIC 2019 - International Conference on Electronics, Information, and Communication. Institute of Electrical and Electronics Engineers Inc. 2019. 8706381. (ICEIC 2019 - International Conference on Electronics, Information, and Communication). doi: 10.23919/ELINFOCOM.2019.8706381

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abstract = "The single shot multi-box detector (SSD) is one of the first real-time detectors, which uses a convolutional neural network (CNN) and achieves the state-of-the-art detection performance. However, owing to the semantic gap between each feature layer of CNN, the SSD has a room for improvement. In this paper, we propose a novel training scheme to enhance the performance of the SSD. In object detection, ground truth (GT) box is a bounding box enclosing an object boundary. To improve the semantic level of the feature map, we generate additional GT boxes by zooming in to and out from the original GT boxes. Experimental results show that the SSD trained with our scheme outperforms the original one on public dataset.",

keywords = "Computer vision, Neural network, Object detection",

author = "Cho, {Sung Jin} and Kim, {Seung Wook} and Uhm, {Kwang Hyun} and Kook, {Hyong Keun} and Ko, {Sung Jea}",

note = "Funding Information: This work was supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program) (10082585, Development of deep learning-based open EV platform technology capable of autonomous driving) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea). Publisher Copyright: {\textcopyright} 2019 Institute of Electronics and Information Engineers (IEIE).; 18th International Conference on Electronics, Information, and Communication, ICEIC 2019 ; Conference date: 22-01-2019 Through 25-01-2019",

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N1 - Funding Information: This work was supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program) (10082585, Development of deep learning-based open EV platform technology capable of autonomous driving) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea). Publisher Copyright: © 2019 Institute of Electronics and Information Engineers (IEIE).

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N2 - The single shot multi-box detector (SSD) is one of the first real-time detectors, which uses a convolutional neural network (CNN) and achieves the state-of-the-art detection performance. However, owing to the semantic gap between each feature layer of CNN, the SSD has a room for improvement. In this paper, we propose a novel training scheme to enhance the performance of the SSD. In object detection, ground truth (GT) box is a bounding box enclosing an object boundary. To improve the semantic level of the feature map, we generate additional GT boxes by zooming in to and out from the original GT boxes. Experimental results show that the SSD trained with our scheme outperforms the original one on public dataset.

AB - The single shot multi-box detector (SSD) is one of the first real-time detectors, which uses a convolutional neural network (CNN) and achieves the state-of-the-art detection performance. However, owing to the semantic gap between each feature layer of CNN, the SSD has a room for improvement. In this paper, we propose a novel training scheme to enhance the performance of the SSD. In object detection, ground truth (GT) box is a bounding box enclosing an object boundary. To improve the semantic level of the feature map, we generate additional GT boxes by zooming in to and out from the original GT boxes. Experimental results show that the SSD trained with our scheme outperforms the original one on public dataset.

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Learning an object detector using zoomed object regions

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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