Abstract
As the demand for accurate vehicle attribute classification continues to grow in applications such as autonomous driving and traffic management, there is an increasing need for advanced deep-learning models capable of handling diverse and correlated attributes. This paper introduces a Multi-Head Deep Learning Model for Vehicle Attributes Classification. The multihead architecture, designed to accommodate various attributes simultaneously, addresses the limitations of traditional methods and single-head models. Leveraging a selected dataset, the model undergoes training focusing on optimizing performance. Evaluation metrics demonstrate the superiority of the proposed approach over single-head models and traditional methods. Results and insights from the experiments underscore the potential of multi-head deep learning in enhancing the accuracy and robustness of vehicle attribute classification. The paper concludes with a discussion of challenges, future directions, and the broader implications of the proposed model in real-world applications.