Bangladeshi Vehicle Number Plate Detection: A Deep Learning Approach with YOLO Models

Abstract

This work presented here compares the performance of various YOLO models, including YOLOv8m, YOLOv12m, YOLOv11m, YOLOv9m, and YOLOv10m. The models are compared in the context of Bangladeshi vehicle number plate detection based on the leading metrics of precision, recall, and mean Average Precision (mAP) at various Intersection over Union (IoU) thresholds. The study conducts using hyper-parameter tuning YOLO models. These experiments show that YOLOv8m is the best with the maximum precision (0.959) and recall (0.922) with mAP@50 as 0.946, and mAP@50-95 as 0.55. This shows the effectiveness of YOLOv8m in detecting vehicle number plates in varied urban environments. Though YOLOv8m is better performing, YOLOv12m and other models like YOLOv11m and YOLOv9m also perform competitively and hence can be used in real-time vehicle number plate detection systems. YOLOv8m's better overall performance highlights its viability in traffic management system and surveillance applications, making it a desirable choice for Bangladeshi vehicle number plate detection software. This work enlightens the application of YOLO models in using them for the task of vehicle detection and extends the limits in urban object detection through deep learning.