Real Time Bangladeshi Vehicle Type Recognition Using YOLOv9 Variants

The paper develops and compares two YOLOv9 variants (YOLOv9-C and YOLOv9-E) for detecting 12 vehicle classes — including non-conventional local vehicles like rickshaws, legunas, easybikes, and wheelbarrows — using a custom annotated dataset collected from real Bangladeshi traffic signal points (402 images, 956 instances). This addresses the gap left by generic Western/East Asian detection datasets, which fail to generalize to Bangladesh’s heterogeneous traffic mix. YOLOv9-E achieved the best overall results (mAP@50 73.66%, precision 0.8508, recall 0.6433, F1 0.7327), while YOLOv9-C showed a marginally tighter mAP@50-95 (45.53%), highlighting an accuracy–localization trade-off between the two variants. The best-performing model was deployed via Streamlit to demonstrate real-time, practical feasibility for traffic monitoring.