Superior Use of YOLOv8 to Enhance Car License Plates Detection Speed and Accuracy

Abstract

Recent advances in computer vision help machines understand and process visual information. Computer vision is commonly used to recognise car licence plates, improving traffic monitoring, law enforcement, and parking management. The use of deep learning has improved object detection accuracy, robustness, and speed. Each algorithm has its own advantages and disadvantages, and the choice often depends on the specific needs or application, such as the need for speed versus the need for high accuracy. In this paper, the YOLOv8 was proposed as an object detecting algorithm. Faster R-CNN (Region-based Convolutional Neural Networks) and SSD (Single Shot Detector) were used to implement, evaluate, and compare their results with the proposed algorithm. The three object identification algorithms utilized car licence plate information from photos and video using frameworks as testing datasets. The results showed that due to its capacity to propose regions and classify objects simultaneously, YOLOv8 is suitable for real-time computer vision workloads. dataset size and hyperparameter values are thoroughly examined to determine model performance. Two datasets of varying sizes were used to evaluate methods. Indian number plate and Automatic Number Plate Recognition use YOLOv8 to optimise precision and recall with f1 confidence curve values of 0.700 for small datasets and 0.419 for large datasets.