Advanced Machine Learning-Driven Automated Multi-Level Parking System

The advancement of Machine Learning (ML) algorithms has enhanced the field of automated parking systems by introducing new methods for standardized parking management. This study leads to transforming parking systems by developing new technological solutions to address the problems with conventional parking methods, such as manual slot allocation and long wait times. By implementing ML into an automated parking framework, this research seeks to create an efficient system to manage parking spaces effectively. The system's main feature is using the ML algorithm Convolutional Neural Network (CNN) for image registration plate recognition, which allows for precise parking spot allocation using this data. The system uses a robust ML algorithm to process incoming vehicle images by strategically placing cameras at access entry points. The accurate recognition and extraction of identifying plate information by the image detection algorithm enables such a user-friendly and real-time method of automobile registration in the parking infrastructure. Parking efficiency and optimal space usage are the key goals of the suggested system. This research leads towards efficient management of multi-level parking infrastructure requiring less human intervention and higher security. The importance of the study endeavor becomes apparent when considering its ability to use innovative ML approaches to revamp conventional parking methodologies completely. This study focuses on a more intelligent, efficient, and secure parking solution that meets the increasing demands of urban infrastructure and modern car management by creating a virtualized autonomous multilevel parking system.