A Study on Fine-Grained Motorcycle Classification for Intelligent Transportation Systems - Lima, Gabriel E.; Santos, Eduardo; Nascimento Jr., Eduil; Laroca, Rayson; Menotti, David (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI), 2025)
Vehicle recognition from images is crucial to Intelligent Transportation System (ITS), supporting applications such as tolling, access control, and forensics. Fine-Grained Vehicle Classification (FGVC) extends this capability by identifying vehicles by make, model, and type. However, research has largely centered on four-wheeled vehicles, with motorcycles receiving limited attention despite representing a substantial share of traffic in many countries, including Brazil. This under-representation can reduce ITS effectiveness and fairness. This work-in-progress study addresses this gap by investigating Fine-Grained Motorcycle Classification (FGMC) in real-world ITS scenarios. We evaluate seven deep learning architectures under two training protocols for independent make and model classification. To enable this, we augment a widely adopted dataset for Automatic License Plate Recognition (ALPR) with motorcycle make and model annotations. Results show that FGMC is feasible within the studied context, yet performance is hindered by severe class imbalance, underscoring the need for improved balancing strategies. The results also reveal a drop in accuracy under challenging conditions, particularly at night or in low-light environments. Future directions include expanding the dataset to more diverse scenarios and exploring FGMC integration with ALPR to enhance overall vehicle identification accuracy.