A 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912409220808704 |
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| author | Kim, Youngmin Kang, Donghwa Baek, Hyeongboo |
| author_facet | Kim, Youngmin Kang, Donghwa Baek, Hyeongboo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01338 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation Kim, Youngmin Kang, Donghwa Baek, Hyeongboo Computer Vision and Pattern Recognition 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. |
| title | A 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.01338 |