the proposed design offers a complete solution to support and surveillance vehicles remotely. The offered algorithm allows a monitoring center to track vehicles; diagnoses fault remotely, control the traffic and control CO emission. The system is programmed to scan the on-board diagnostic OBD periodically or based on request to check if there are any faults and read all the available sensors, then make an early fault prediction based on the sensor readings, an experience with the vehicle type and fault history. It is so useful for people who are not familiar with fault diagnosis as well as the maintenance center. The system offers tracking the vehicle remotely, which protects it against theft and warn the driver if it exceeds the speed limit according to its location. Finally, it allows the user to report any traffic congestion and allow s a vehicle navigator to be up to date with the traffic condition based on the other system’s user feedback.
License plate recognition is an essential part of contemporary surveillance systems since it is helpful in many applications, including parking management, vehicle access control, traffic control, and law enforcement. This project aims to provide a robust and dependable method for detecting license plates that will outperform existing approaches in accuracy and dependability. This observation method uses contemporary technology to address challenging troubles related to license plate recognition. Our methodology is primarily based on the Faster R-CNN structure, an established model for picture item detection. The novel thing, even though, is how Gray Wolf Optimization—which draws notion from the searching conduct of gray wolves—is mixed with the Faster R-CNN network. The accuracy is greatly improved by this synergistic combination, which also improves detection abilities. Moreover, an improved ResNet-50 model is blanketed to improve the classification system similarly, ensuring accurate license plate detection in several situations. The extensively utilized ”car license plate detection” dataset is used to assess the recommended technology very well, confirming its efficacy in practical settings. The empirical outcomes show exceptional performance, with a median precision of 98.21%, demonstrating how nicely the hybrid method works to attain the very best stage of license plate detecting accuracy. This painting establishes a new benchmark in license plate identity using cutting-edge technology and innovative techniques, starting the door for enhanced safety and surveillance.