Recognition of Car Front Facing Style for Machine-Learning Data Annotation: A Quantitative Approach

Abstract

Car front facing style (CFFS) recognition is crucial to enhancing a company’s market competitiveness and brand image. However, there is a problem impeding its development: with the sudden increase in style design information, the traditional methods, based on feature calculation, are insufficient to quickly handle style analysis with a large volume of data. Therefore, we introduced a deep feature-based machine learning approach to solve the problem. Datasets are the basis of machine learning, but there is a lack of references for car style data annotations, which can lead to unreliable style data annotation. Therefore, a CFFS recognition method was proposed for machine-learning data annotation. Specifically, this study proposes a hierarchical model for analyzing CFFS style from the morphological perspective of layout, surface, graphics, and line. Based on the quantitative percentage of the three elements of style, this paper categorizes the CFFS into eight basic types of style and distinguishes the styles by expert analysis to summarize the characteristics of each layout, shape surface, and graphics. We use imagery diagrams and typical CFFS examples and characteristic laws of each style as annotation references to guide manual annotation data. This investigation established a CFFS dataset with eight types of style. The method was evaluated from a design perspective; we found that the accuracy obtained when using this method for CFFS data annotation exceeded that obtained when not using this method by 32.03%. Meanwhile, we used Vgg19, ResNet, ViT, MAE, and MLP-Mixer, five classic classifiers, to classify the dataset; the average accuracy rates were 76.75%, 78.47%, 78.07%, 75.80%, and 81.06%. This method effectively transforms human design knowledge into machine-understandable structured knowledge. There is a symmetric transformation of knowledge in the computer-aided design process, providing a reference for machine learning to deal with abstract style problems.