Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Journal of university of Anbar for pure science

DOI: 10.37652/juaps.2025.161328.1428

Authors: Anwar Al-Saleh, Mohanad Meteab, Shajan Mahdi, Ali Al-Zuky

Institution: Mustansiriyah University

Language: English

Topic: Vehicle License Plate Recognition

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W7148452657