Design and Implementation of Advance Real Time Parking System

Abstract

Effective parking facility management is critical to the functioning of urban infrastructure, but current systems frequently struggle to reliably identify occupants and neutralize possible threats. In this paper, we suggest a brand-new parking management strategy. The title is "Advanced Real-Time Parking System" . A strong parking management system with machine learning-based threat detection is presented in this paper. Three specialized models were created using YOLO v8 and trained on carefully selected datasets of 800, 800, and 600 images that included license plates, parking occupancy, fires, and explosions. By taking advantage of YOLO v8's flexibility, these models were designed with high detection accuracy in mind. The models underwent thorough training and validation before being smoothly incorporated into a single system that provided extensive surveillance capabilities. The evaluation showed a remarkable accuracy of 92%, demonstrating the effectiveness of the system in actual threat detection situations. This study highlights the potential of YOLO v8-based solutions to improve parking environments' safety and security, demonstrating their adaptability in tackling a range of security issues.