2025/09/29 by Abu Hanif Muhammad Syarubany, Syarubany, Abu Hanif Muhammad
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Currency Recognition and Detection #FOS: Computer and information sciences #Vehicle License Plate Recognition
paper · pdf · doi:10.48550/arxiv.2509.24880
openalex publication_date 2025/09/29 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/28
Accurate vehicle type recognition underpins intelligent transportation and logistics, but severe class imbalance in public datasets suppresses performance on rare categories. We curate a 16-class corpus (~47k images) by merging Kaggle, ImageNet, and web-crawled data, and create six balanced variants via SMOTE oversampling and targeted undersampling. Lightweight ensembles, such as Random Forest, AdaBoost, and a soft-voting combiner built on MobileNet-V2 features are benchmarked against a configurable ResNet-style CNN trained with strong augmentation and label smoothing. The best ensemble (SMOTE-combined) attains 74.8% test accuracy, while the CNN achieves 79.19% on the full test set and 81.25% on an unseen inference batch, confirming the advantage of deep models. Nonetheless, the most under-represented class (Barge) remains a failure mode, highlighting the limits of rebalancing alone. Results suggest prioritizing additional minority-class collection and cost-sensitive objectives (e.g., focal loss) and exploring hybrid ensemble or CNN pipelines to combine interpretability with representational power.