Image Classification to Identify Style Composition Ratios in Crossover Cars
As the global demand for multifunctional and high-performance vehicles increases, automotive manufacturers face significant challenges in designing new crossover models. Consumers expect vehicles to blend features from various car models, which pushes the industry to adopt innovative design tools an...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/89/1/26 |
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| Summary: | As the global demand for multifunctional and high-performance vehicles increases, automotive manufacturers face significant challenges in designing new crossover models. Consumers expect vehicles to blend features from various car models, which pushes the industry to adopt innovative design tools and methods. We explored the use of Waikato Environment for Knowledge Analysis (WEKA) image classification to predict the style composition ratios of sedans, hatchbacks, multi-purpose vehicles (MPVs), and sport utility vehicles (SUVs) as crossover vehicles. We collected 240 high-resolution side-view images of luxury vehicles from brands including Mercedes-Benz, BMW, and Lexus, and preprocessed the data using format unification and feature enhancement. We employed WEKA to extract image features and train a classification model using the edge histogram filter and sequential minimal optimization (SMO) classifier, which achieved an 86% classification accuracy. Subsequently, we used Vizcom, a generative Artificial Intelligence(AI) tool, to simulate realistic designs for new crossover cars and predict their style composition ratios. The proposed designs were evaluated by five experts, who found that the model accurately identified style composition ratios and helped designers create new car styles with market potential. The novel application of image classification can be used for analyzing blended styles in automotive design and enables designers to identify, evaluate, and control styles to meet market demands. |
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| ISSN: | 2673-4591 |