用逼真合成图像提升车辆分类与朝向检测精度
A 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation
- 分两阶段：先生成真实风格图像，再融合信息检测
- 在真实数据上实现4分（IEEE BigData挑战赛排名）
- 适合需要高精度车辆识别的自动驾驶场景
我们旨在通过合成数据训练模型来检测车辆类别和朝向。然而，训练数据中类别分布不均，且合成图像训练的模型在真实图像上表现不佳。为此，我们提出一种两阶段检测模型，结合逼真图像生成技术解决该问题。模型共分四步：(1) 构建包含图像、类别及物体位置信息的元表；(2) 将合成图像转换为真实风格，并合并至元表；(3) 利用元表中的图像进行车辆类别与朝向分类；(4) 结合预提取的位置信息与预测类别完成最终检测。本方法在 IEEE BigData Challenge 2022 车辆类别与朝向检测（VOD）任务中取得第4名。
原文摘要 · Abstract (English)
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.
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