Dynamic Parameter Initialization and Optimization for Vehicular Image Recognition via Meta-Learning and Model Pruning

This study is dedicated to solving the problem of image recognition in vehicular networks, with a particular focus on two key scenarios: traffic sign and vehicle classification. To achieve this, we developed an advanced deep learning model that integrates both meta-learning and model pruning techniques. Meta-learning is used for the efficient initialization of model parameters, thereby enhancing the model’s generalization capabilities. Model pruning, on the other hand, optimizes the computational efficiency of the model by removing unnecessary parameters to reduce its complexity. After rigorous theoretical analysis and experimental validation, the results on two crucial datasets showed significant performance improvements. Compared to the baseline model, which did not apply meta-learning and model pruning, our model improved the accuracy rates by 2.7% and 2.9%, respectively. This progress not only proves the effectiveness of these two optimization techniques in vehicular network applications but also provides valuable insights for future research in this field. Overall, this study not only addresses specific image recognition issues in vehicular networks but also demonstrates the potential and effectiveness of applying advanced techniques like meta-learning and model pruning in real-world scenarios. These achievements contribute to the advancement of image recognition technology in vehicular networks and offer new perspectives and practical methods for further research in the same domain.