论文标题
GP22：汽车设计师的汽车造型数据集
GP22: A Car Styling Dataset for Automotive Designers
论文作者
论文摘要
自动化的设计数据归档可以减少设计师从创造性上有效工作浪费的时间。尽管存在许多有关对CAR外观进行分类，检测和实例分段的数据集，但这些大数据集与设计实践无关，因为主要目的在于自动驾驶或车辆验证。因此，我们发布了由汽车设计师定义的汽车样式功能组成的GP22。该数据集包含来自37个品牌和十个车段的1480个汽车侧面配置图像。它还包含遵循汽车外部设计特征的分类学特征的设计功能的注释，该特征在汽车设计师眼中定义。我们使用Yolo V5作为数据集的设计特征检测模型训练了基线模型。提出的模型的地图得分为0.995，召回0.984。此外，在草图上探索模型性能以及渲染汽车侧剖面的图像意味着数据集的可扩展性是为了设计目的。
An automated design data archiving could reduce the time wasted by designers from working creatively and effectively. Though many datasets on classifying, detecting, and instance segmenting on car exterior exist, these large datasets are not relevant for design practices as the primary purpose lies in autonomous driving or vehicle verification. Therefore, we release GP22, composed of car styling features defined by automotive designers. The dataset contains 1480 car side profile images from 37 brands and ten car segments. It also contains annotations of design features that follow the taxonomy of the car exterior design features defined in the eye of the automotive designer. We trained the baseline model using YOLO v5 as the design feature detection model with the dataset. The presented model resulted in an mAP score of 0.995 and a recall of 0.984. Furthermore, exploration of the model performance on sketches and rendering images of the car side profile implies the scalability of the dataset for design purposes.
