A 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation

Youngmin Kim; Donghwa Kang; Hyeongboo Baek

doi:10.1109/BigData55660.2022.10020472

A 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation

Youngmin Kim, Donghwa Kang, Hyeongboo Baek

Department of Artificial Intelligence

Incheon National University

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

Abstract

We aim to detect the class and orientation of a vehicle by training a model with synthetic data. However, the distribution of the classes in the training data is imbalanced, and the model trained on the synthetic image is difficult to predict in real-world images. We propose a two-stage detection model with photo-realistic image generation to tackle this issue. Our model mainly takes four steps to detect the class and orientation of the vehicle. (1) It builds a table containing the image, class, and location information of objects in the image, (2) transforms the synthetic images into real-world images style, and merges them into the meta table. (3) Classify vehicle class and orientation using images from the meta-table. (4) Finally, the vehicle class and orientation are detected by combining the pre-extracted location information and the predicted classes. We achieved 4th place in IEEE BigData Challenge 2022 Vehicle class and Orientation Detection (VOD) with our approach. Our code and project material will be available at https://github.com/inu-RAISE/VOD_Challenge

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
Editors	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6489-6493
Number of pages	5
ISBN (Electronic)	9781665480451
DOIs	https://doi.org/10.1109/BigData55660.2022.10020472
State	Published - 2022
Event	2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan Duration: 17 Dec 2022 → 20 Dec 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference	2022 IEEE International Conference on Big Data, Big Data 2022
Country/Territory	Japan
City	Osaka
Period	17/12/22 → 20/12/22

Keywords

Classification
GAN
Image-to-Image Translation
Object Detection

Access to Document

10.1109/BigData55660.2022.10020472

Cite this

Kim, Y., Kang, D., & Baek, H. (2022). A 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (pp. 6489-6493). (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData55660.2022.10020472

Kim, Youngmin ; Kang, Donghwa ; Baek, Hyeongboo. / A 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 6489-6493 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

@inproceedings{d92a4dd3bf874bf5bd0ae41e90a855b7,

title = "A 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation",

abstract = "We aim to detect the class and orientation of a vehicle by training a model with synthetic data. However, the distribution of the classes in the training data is imbalanced, and the model trained on the synthetic image is difficult to predict in real-world images. We propose a two-stage detection model with photo-realistic image generation to tackle this issue. Our model mainly takes four steps to detect the class and orientation of the vehicle. (1) It builds a table containing the image, class, and location information of objects in the image, (2) transforms the synthetic images into real-world images style, and merges them into the meta table. (3) Classify vehicle class and orientation using images from the meta-table. (4) Finally, the vehicle class and orientation are detected by combining the pre-extracted location information and the predicted classes. We achieved 4th place in IEEE BigData Challenge 2022 Vehicle class and Orientation Detection (VOD) with our approach. Our code and project material will be available at https://github.com/inu-RAISE/VOD_Challenge",

keywords = "Classification, GAN, Image-to-Image Translation, Object Detection",

author = "Youngmin Kim and Donghwa Kang and Hyeongboo Baek",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",

year = "2022",

doi = "10.1109/BigData55660.2022.10020472",

language = "English",

series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

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pages = "6489--6493",

editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",

booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

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Kim, Y, Kang, D & Baek, H 2022, A 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, Institute of Electrical and Electronics Engineers Inc., pp. 6489-6493, 2022 IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, 17/12/22. https://doi.org/10.1109/BigData55660.2022.10020472

A 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation. / Kim, Youngmin; Kang, Donghwa; Baek, Hyeongboo.
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. p. 6489-6493 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

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

TY - GEN

T1 - A 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation

AU - Kim, Youngmin

AU - Kang, Donghwa

AU - Baek, Hyeongboo

PY - 2022

Y1 - 2022

N2 - We aim to detect the class and orientation of a vehicle by training a model with synthetic data. However, the distribution of the classes in the training data is imbalanced, and the model trained on the synthetic image is difficult to predict in real-world images. We propose a two-stage detection model with photo-realistic image generation to tackle this issue. Our model mainly takes four steps to detect the class and orientation of the vehicle. (1) It builds a table containing the image, class, and location information of objects in the image, (2) transforms the synthetic images into real-world images style, and merges them into the meta table. (3) Classify vehicle class and orientation using images from the meta-table. (4) Finally, the vehicle class and orientation are detected by combining the pre-extracted location information and the predicted classes. We achieved 4th place in IEEE BigData Challenge 2022 Vehicle class and Orientation Detection (VOD) with our approach. Our code and project material will be available at https://github.com/inu-RAISE/VOD_Challenge

AB - We aim to detect the class and orientation of a vehicle by training a model with synthetic data. However, the distribution of the classes in the training data is imbalanced, and the model trained on the synthetic image is difficult to predict in real-world images. We propose a two-stage detection model with photo-realistic image generation to tackle this issue. Our model mainly takes four steps to detect the class and orientation of the vehicle. (1) It builds a table containing the image, class, and location information of objects in the image, (2) transforms the synthetic images into real-world images style, and merges them into the meta table. (3) Classify vehicle class and orientation using images from the meta-table. (4) Finally, the vehicle class and orientation are detected by combining the pre-extracted location information and the predicted classes. We achieved 4th place in IEEE BigData Challenge 2022 Vehicle class and Orientation Detection (VOD) with our approach. Our code and project material will be available at https://github.com/inu-RAISE/VOD_Challenge

KW - Classification

KW - GAN

KW - Image-to-Image Translation

KW - Object Detection

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M3 - Conference contribution

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BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

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A2 - Ohsawa, Yukio

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A2 - Motomura, Yoichi

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PB - Institute of Electrical and Electronics Engineers Inc.

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Kim Y, Kang D, Baek H. A 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation. In Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, editors, Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 6489-6493. (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). doi: 10.1109/BigData55660.2022.10020472

A 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation

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Publication series

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Keywords

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