1*Professor & Head, Department of CSE (IOT & CS Using Block Chain Technology), Brindavan College of Engineering, Bangalore 560063.
Abstract— An AI-powered car number plate recognition system for intelligent traffic management and monitoring is presented in this research. The system uses the YOLO deep learning framework to quickly and accurately detect license plates in a variety of metropolitan settings with different lighting and environmental conditions. For accurate identification, the method makes use of a carefully selected dataset with 433 annotated car photos and employs a thorough pipeline that includes image preprocessing, license plate region extraction, character segmentation, and optical character recognition. Its efficacy in real-time applications is validated by experimental results, which show strong performance with precision reaching 0.894, recall at 0.818, and mAP50 at 0.898. By offering scalability, flexibility for various plate formats, and resilience against occlusions and distortions, the system overcomes issues with traditional approaches. By lowering manual involvement and boosting accuracy and efficiency, its implementation can greatly improve automated traffic enforcement, vehicle monitoring, and security surveillance. By using state-of-the-art AI approaches, this research promotes intelligent transportation systems and enables safer and more intelligent traffic monitoring solutions worldwide.
Keywords: YOLO Framework, Deep Learning, and Vehicle Number Plate Recognition Detection of license plates, optical character recognition, traffic monitoring, Traffic Analysis in Real Time, Intelligent Transportation Automated Vehicle Recognition, Image Preparation, Annotation of Datasets
How to cite this article: Vijayaraghavan A. AI-Powered Vehicle Number Plate Recognition for Smart Traffic Monitoring. Int J Drug Deliv Technol. 2026;16(16s): 178-185. DOI: 10.25258/ijddt.16.16s.18
Source of support: Nil.
Conflict of interest: None