Optimizing Car License Plate Recognition Through Gray Wolf Optimization Algorithm - Aty, Ahmed; Mohasefi, Jamshid (Iraqi Journal for Electrical and Electronic Engineering, 2025)
License plate recognition is an essential part of contemporary surveillance systems since it is helpful in many applications,including parking management, vehicle access control, traffic control, and law enforcement. This project aims to providea robust and dependable method for detecting license plates that will outperform existing approaches in accuracyand dependability. This observation method uses contemporary technology to address challenging troubles related tolicense plate recognition. Our methodology is primarily based on the Faster R-CNN structure, an established modelfor picture item detection. The novel thing, even though, is how Gray Wolf Optimization—which draws notion fromthe searching conduct of gray wolves—is mixed with the Faster R-CNN network. The accuracy is greatly improvedby this synergistic combination, which also improves detection abilities. Moreover, an improved ResNet-50 model isblanketed to improve the classification system similarly, ensuring accurate license plate detection in several situations.The extensively utilized ”car license plate detection” dataset is used to assess the recommended technology very well,confirming its efficacy in practical settings. The empirical outcomes show exceptional performance, with a medianprecision of 98.21%, demonstrating how nicely the hybrid method works to attain the very best stage of license platedetecting accuracy. This painting establishes a new benchmark in license plate identity using cutting-edge technologyand innovative techniques, starting the door for enhanced safety and surveillance.