  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.
