Enhancing Vehicle Entrance and Parking Management: Deep Learning Solutions for Efficiency and Security

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 <PRE_TAG>vehicle detection</POST_TAG>, license number plate verification, and <PRE_TAG>face detection</POST_TAG> and <PRE_TAG>recognition</POST_TAG> models to ensure that the person and vehicle are registered with the organization. We have trained multiple deep-learning models for <PRE_TAG>vehicle detection</POST_TAG>, license number plate detection, <PRE_TAG>face detection</POST_TAG>, and <PRE_TAG>recognition</POST_TAG>, however, the <PRE_TAG>YOLOv8n</POST_TAG> model outperformed all the other models. Furthermore, License plate <PRE_TAG>recognition</POST_TAG> is facilitated by Google's <PRE_TAG>Tesseract-OCR Engine</POST_TAG>. By integrating these technologies, the system offers efficient <PRE_TAG>vehicle detection</POST_TAG>, 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.