Enhancing Vehicle Entrance and Parking Management: Deep Learning Solutions for Efficiency and Security
This addresses efficiency and security issues for organizations managing vehicle access and parking, but it is incremental as it combines existing deep learning methods.
The paper tackled the problem of automating vehicle entrance and parking management in organizations by developing a system that integrates deep learning models for vehicle detection, license plate verification, and face recognition, achieving efficient detection and identification with YOLOv8n outperforming other models.
The auto-management of vehicle entrance and parking in any organization is a complex challenge encompassing record-keeping, efficiency, and security concerns. Manual methods for tracking vehicles and finding parking spaces are slow and a waste of time. To solve the problem of auto management of vehicle entrance and parking, we have utilized state-of-the-art deep learning models and automated the process of vehicle entrance and parking into any organization. To ensure security, our system integrated vehicle detection, license number plate verification, and face detection and recognition models to ensure that the person and vehicle are registered with the organization. We have trained multiple deep-learning models for vehicle detection, license number plate detection, face detection, and recognition, however, the YOLOv8n model outperformed all the other models. Furthermore, License plate recognition is facilitated by Google's Tesseract-OCR Engine. By integrating these technologies, the system offers efficient vehicle detection, precise identification, streamlined record keeping, and optimized parking slot allocation in buildings, thereby enhancing convenience, accuracy, and security. Future research opportunities lie in fine-tuning system performance for a wide range of real-world applications.