Region-Specific Vehicle Classification in Bangladesh: A Comparative Study of CNN Architectures and Ensemble Strategies

The rapid growth of vehicles in Bangladesh has exacerbated traffic congestion, safety concerns, and administrative difficulties, creating an urgent need for Intelligent Transportation Systems (ITS). However, existing global datasets poorly represent Bangladeshi vehicles, and no region-specific detection models are currently available. To address this gap, this research constructs a balanced augmented dataset combining two public repositories, yielding images across ten vehicle classes. A harmonized dataset of manually verified images was split prior to augmentation, expanding the training set to 24,696 samples. Five deep Convolutional Neural Network architectures are then evaluated using transfer learning and extensive augmentation, and a convex ensemble of three selected models with optimized weights is developed to enhance robustness. The single ConvNeXt model achieves 98.980% ± 0.198% accuracy, while the ensemble attains 98.991% ± 0.156% accuracy. Statistical testing confirms the significance of these results; the ensemble effectively reduces misclassifications between visually similar categories. Overall, the proposed system provides a dependable platform for transportation applications in Bangladesh. Future work will address current limitations in static image and category scope through video-based analysis and lightweight deployment strategies.