Abstract

This paper tackles worldwide road-safety and traffic-management issues by implementing vehicle speed detection and license plate identification technologies. A holistic method to improve road-safety and traffic control addresses limited training data issues. The study stresses the necessity for an efficient and reliable vehicle recognition, license plate identification, and character segmentation system for precise speed detection. A three-class Vehicle Detection model and customized models for Numberplate detection, Character-segmentation, and Character-Detection are presented to suit this need. Creating complete training and testing datasets requires thorough data preparation, hand clipping, and labeling. Data augmentation separates validation and testing subsets while expanding the dataset. A robust and automatic system for real-time vehicle speed detection and license plate identification is the major contribution of this research. The suggested system uses advanced deep learning to monitor and regulate traffic efficiently, reducing manual intervention and improving road-safety. Experimental findings reveal that the Vehicle Detection model can recognize automobiles and the specialized models can detect license plates, segment characters, and detect characters. The output of one model feeds into the input of another on a distributed system, thus these four models can operate simultaneously. These results demonstrate the system's ability to improve road-safety and urban traffic management.