A Hybrid Deep Learning VGG-16 Based SVM Model for Vehicle Type Classification
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Abstract
Car classification is important in daily life because there are many distinct types of automobiles made by various manufacturers. Although there are numerous methods for classifying autos, machine learning technologies have not been widely utilized, resulting in low accuracy levels. The goal of this paper is to create a machine learning system that is especially made to categories models of two Pakistan's top automakers, Toyota, and Honda. Ten Toyota models such as Avalon, Land Cruiser, Camry, Corolla, C-HR, Highlander, Prius, Tundra, RAV4, and Yaris and a dataset of Honda automobiles, which also includes 10 models (Accord, Civic, CR-V, Fit, HR-V, Insight, Odyssey, Passport, Pilot, and Ridgeline), are used to evaluate the model's performance. A deep learning-based VGG integrated with support vector machine (SVM) is proposed, utilizing a dataset from Kaggle.com, providing high-definition images for multiple classes. Comparisons with other models such as VGG16, AlexNet, and Convolutional Neural Network (CNN) reveal that the suggested model (VGG16 + SVM) achieves superior accuracy. For the Toyota dataset, the proposed model achieves 99% accuracy, outperforming VGG16 (66%), AlexNet (52%), and CNN (65%). Similarly, for the Honda dataset, the suggested model achieves 98% accuracy, surpassing VGG16 (96%), AlexNet (71%), and CNN (82%). In conclusion, the proposed deep learning-based model demonstrates enhanced accuracy in classifying Toyota and Honda cars, highlighting its effectiveness for image-based classification tasks in the automotive domain.
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