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.

本研究提出一种自动化设计数据归档的方案，介绍了一个由汽车设计师定义的汽车外观设计特征数据集 GP22，并使用 YOLO v5 作为设计特征检测模型进行基线模型训练以及性能测试，结果表明该数据集可在设计领域中应用。