<![CDATA[AVNPR systems are critical in intelligent transportation, monitoring, and law enforcement systems. Nevertheless, the current systems are usually challenged by the issues of dissimilar illumination, obstruction, and the diversity of plate formats, which restrict their practical applicability. To solve these problems, this paper suggests a real-time deep learning-driven AVNPR framework that incorporates effective detection and recognition systems. The proposed system employs the YOLOv8 object detector model to localize number plates with high accuracy and speed, as well as a lightweight recognition module to identify alphanumeric characters. A custom dataset with different types of vehicles in different environmental conditions was created and improved with the help of preprocessing and data augmentation methods to make the model more robust. In the experiments, the proposed system demonstrated an overall system accuracy of 98.7%, representing the combined number plate detection and character recognition results. The mAP@0.5 is 97%, and mAP 0.5-0.95 is 91%, as well as high precision, recall, and F1-score, which suggests that it shows potential applicability across varying conditions in the assessed dataset and suggests that it may be suitable for real-world applications. The system is also implemented with a Flask-based web application, and it supports image based and real-time webcam detection. The results indicate that the proposed framework provides a viable, efficient, and deployable solution to AVNPR applications. The work will lead to the creation of scalable and real-time intelligent transportation systems and give a basis for future advancement in the improvement of robust vehicle recognition in challenging conditions.]]>
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