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

Youngmin Kim, Donghwa Kang, Hyeongboo Baek

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 4 ^th 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