We present a modular multi-attribute vehicle analysis pipeline that integrates YOLO-based models and an OCR engine into a single workflow. The system detects vehicles, classifies color, recognizes make and sub-model, detects license plates, and extracts plate characters to generate a structured vehicle record. Vehicle detection is reported with standard metrics (precision, recall, and mAP@0.5), while license plate detection is reported at IoU = 0.3 to reflect the small-object nature of plates and downstream OCR usability. Among the evaluated versions, YOLOv8 provides the most balanced overall performance across modules, while maintaining real-time-equivalent throughput of approximately 18–22 FPS for the full pipeline on recorded traffic videos, depending on scene complexity. We emphasize module-level evaluation and runtime benchmarking; instance-level end-to-end identification across unique vehicles is defined as future work once track-based ground truth becomes available.
Building similarity graph...
Analyzing shared references across papers
Loading...
Cristian Japhet Islas-Yañez
Instituto Politécnico Nacional
Viridiana Hernández-Herrera
Instituto Politécnico Nacional
Moisés Márquez-Olivera
Instituto Politécnico Nacional
Sensors
Instituto Politécnico Nacional
Building similarity graph...
Analyzing shared references across papers
Loading...
Islas-Yañez et al. (Wed,) studied this question.
synapsesocial.com/papers/69f837ab3ed186a739981eba — DOI: https://doi.org/10.3390/s26092785
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: