Integrating computer vision, web systems and embedded systems to develop an intelligent monitoring system for violating vehicles

Abstract

This study aims to design and develop a smart system based on artificial intelligence and deep learning techniques to monitor and track violating vehicles, especially those driving in the opposite direction or illegally on the roads. The study relies on the use of the YOLOv8 algorithm, which has proven to be highly efficient compared to other algorithms such as RCNN, to determine the location of the target vehicle in real time. The proposed system seeks to integrate computer vision, web development, and embedded systems technologies into one integrated and efficient system, which includes connecting to a database to update the location in real time, in addition to an alarm system using Arduino technology to send notifications about the monitored condition. By implementing the system, the researchers hope to reduce traffic violations and reduce accidents related to driving.