Vehicle Licence Number Plate Recognition Using Convolution Neural Network for Traffic Violators in Indonesia

Abstract

In the context of rising traffic violations and the need for efficient traffic management, this study explores the application of CNN in the recognition of licence plates to identify traffic violators in Indonesia. Traditional traffic enforcement methods are labour-intensive and prone to human error, necessitating a more automated and reliable approach. This research aims to enhance the accuracy and efficiency of license plate recognition (LPR) systems. The proposed system involves capturing vehicle images from the Roboflow Universe collected in the Malang area for use. We also use a CNN model to recognize and extract the alphanumeric characters from the plates. The CNN architecture is designed and trained on a comprehensive dataset of Indonesian licence plates, taking into account the unique characteristics and variations in plate designs specific to the region. The research we are doing is detecting number plates to reduce traffic violations. The method used for detection is the CNN method. The datasets used are primary and secondary. The precision, recall, and F1 score metrics further validate the system's reliability and robustness in real-world traffic scenarios. The implementation of this CNN-based LPR system promises a substantial improvement in monitoring and penalizing traffic violators, contributing to better traffic law enforcement and road safety in Indonesia. The accuracy for CRR is 82, and the accuracy for LPR is 85,33. The accuracy for CCR is 76.55 for precisions, 78.51 for recall, and 81.72 for F1 score. The accuracy for LPR is 81.20 for precision, 87.37 for recall, and 83.56 for F1 score.