Modified YOLOv5-based License Plate Detection: an Efficient Approach for Automatic Vehicle Identification

Abstract

Indonesia witnesses a continual annual surge in the vehicle count, with the Central Statistics Agency (BPS) projecting a total of 148.2 million vehicles in 2022, marking a 6.3 million increase from the preceding year. This growth underscores the escalating challenges associated with traffic management and violations. Hence, the development of a robust vehicle number plate image recognition system becomes paramount for effective traffic control, accurate parking records, and streamlined identification of vehicle owners. In this study, the YOLO v5 algorithm is employed in conjunction with the AOLP dataset, encompassing diverse vehicle images under challenging conditions, such as low lighting, intricate viewing angles, and blurred license plates. The YOLO v5 algorithm exhibits noteworthy performance metrics, boasting a recall value of 99.7%, precision reaching 99.1%, mAP50 of 99.4%, and mAP50-95 of 84.8%. The elevated precision signifies the model’s proficiency in minimizing identification errors, while the commendable recall highlights its adeptness in locating existing number plates accurately. Concurrently, the Optical Character Recognition (OCR) model, dedicated to character recognition on number plates, attains an accuracy level of 92.85%, underscoring its efficacy in deciphering alphanumeric characters. This integrated approach leverages advanced algorithms to tackle the intricacies of realworld scenarios, affirming its viability for enhancing traffic management systems and bolstering the efficiency of vehicle-related processes.