Fine-Grained Vehicle Make and Model Recognition for Smart City Environmental Monitoring: A YOLO11-Based Two-Stage Framework
Aya Elouali, Antonio J. Jara · Urban Science · 2026
Accurate, real-time vehicle identification is essential for data-driven urban planning, enabling applications like emissions monitoring and the enforcement of environmental regulations. However, identifying a vehicle’s model, generation, and production year remains a significant challenge for VMMR systems. This is especially true when cameras capture multiple vehicles simultaneously under suboptimal imaging conditions. This challenge is amplified in Europe, as most existing VMMR datasets are designed for non-European markets. To address this, we present two contributions: a newly curated dataset of 84,732 images across 625 classes, and a robust two-stage YOLO11 system trained on this data. The dataset focuses on the European market and realistic viewpoints like front and rear angles. The system, comprising a Vehicle Localization Module (VLM) and a Fine-Grained Classification Module (FGCM), performs detailed model classification without relying on license plates or additional sensors. When tested on real European traffic footage, our system achieved 80% accuracy and outperformed models trained on U.S.-centric datasets.