Comparative Performance Study of YOLO Models in Bangladeshi Number Plate Detection

Abstract

The paper is a comparison of the performance of selected YOLO models (YOLOv8m, YOLOv12m, YOLOv11m, YOLOv9m, and YOLOv10m) that are used to detect vehicle number plates in Bangladesh with respect to precision, recall and mean Average Precision (mAP) at various Intersection over Union (IoU) thresholds. Following the hyper-parameter tuning, YOLOv8m yields the best performance with a higher accuracy (0.959) and recall (0.922), mAP50 of 0.946 and mAP50-95 of 0.55, which confirms its suitability to have in urban areas. Even though YOLOv8m is better compared to others, there are other models such as YOLOv12m, YOLOv11m, and YOLOv9m, which perform well and can be used as well in real-time number plate recognition systems. A comparative performance analysis presented in this paper covers a broader spectrum of vehicle detection tasks and reveals the evidence of strengths and weaknesses of each of the models. The results indicate that the YOLOv8m can adapt to the traffic infrastructure and urban surveillance systems in Bangladesh especially effectively, demonstrating the future opportunities of using deep learning-based object detection in addressing complicated issues in urban settings.