A new procedure for automatic vehicle license plate recognition monitoring system via CNN and RCNN and Arduino

Ensuring security for vehicle access control in vital institutions, factories, and residential complexes has become a pressing concern. To address this, leading global logistics companies have focused on enhancing security infrastructure through vehicle license plate recognition systems. This research employs two algorithms: Region-based Convolutional Neural Network (RCNN) and Convolutional Neural Network (CNN). Both deep learning models are essential and are widely applied for vehicle detection and license plate recognition in image processing tasks. The system integrates an Arduino microcontroller, an ultrasonic sensor, and a computer-connected camera to capture images of vehicles at checkpoints. These images are then compared with a database of authorized vehicles. Arduino provides an efficient and adaptable platform for object detection and classification across different domains, including robotics, surveillance, and automation, enabling the development of more advanced systems in the future. The system achieved an accuracy rate of over 98%, demonstrating its reliability and effectiveness. This study makes a significant contribution by providing a robust security strategy.