Automatic Number Plate Detection Using Yolov8

Authors: Tanuj Kumar Kusmi, Manish Kumar, Vijay Kumar Gautam, Martand Mishra, Abishek Kumar Singh, Satyam Kumar

Abstract: The traffic within cities has become worse. So we should implement automatic number plate recognition (ANPR). It can be used for traffic management, toll collection, police help and smarter. It has been proved that, some traditional image processing techniques and some of the deep learning approaches have not shown success due to their limitations like lighting condition, blocked license plates, and skewed license plates. This project proposes a system that utilizes YOLOv8. This system uses YOLOv8 and character recognition to detect license plates in the images and videos. So, it acts as an "eye" for identifying license plates. Automatic Number Plate Detection system, has demonstrated success in locating license plates, and it also performs efficiently under various lighting conditions including night, even under blocked plates. The combination of YOLOv8 and character recognition techniques offers a real-time solution for detection and identification of license plates in videos and images for effective traffic management and police assistance for instance tracing vehicles through[27]. The system has been designed to work for global number plates and can prove to be an effective solution for license plate detection with its fast speed technology. Good for Parking systems, toll collection and traffic management. Managing city traffic is a critical issue today, an effective Automatic Number Plate Detection system like the one using YOLOv8 and number plate could provide an efficient solution for this issue using number plates. Parking systems and toll collection can really benefit from this. Managing the traffic in a city is a problem nowadays. A good Automatic Number Plate Detection system that uses YOLOv8 and number plates can help solve this problem. work well with. To make the model work better some techniques have been used. These include flipping the image making it bigger or smaller and changing the brightness. This helps the model to work in different situations. The system can detect number plates fast and it is very accurate. It can detect number plates with an accuracy of 96.4%. It can process over 60 frames per second on a standard computer. This is very good, for Automatic Number Plate Detection systems that use number plates. The YOLOv8-based ANPR system does a job on benchmark datasets and real-world test scenarios. It is better than systems that use CNN or earlier YOLO versions when it comes to how fast it detects things how accurate it is and how well it works in different situations. The YOLOv8-based ANPR system can handle license plates that're at an angle partly covered or different sizes and it works well in various lighting conditions[16].

DOI: http://doi.org/10.5281/zenodo.20524663