Next-Gen Vehicle Attribute Classification: A Multi-head Deep Learning Approach

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.