Putri, Devita Maulina ORCID: https://orcid.org/0000-0003-2300-5632, Jatmiko, Andriyan Rizki, Putra, Firnanda Al-Islama Achyunda and Al-Fath, Ardhillah Habibi Al (2024) Vehicle Licence Number Plate Recognition Using Convolution Neural Network for Traffic Violators in Indonesia. Inform: Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi, 9 (2). pp. 181-186. ISSN P-ISSN : 2502-3470 ; E-ISSN : 2581-0367

Preview	Text Vehicle Licence Number Plate Recognition.pdf Download (374kB) | Preview
Preview	Text Hasil Cek Plagiasi_Vehicle Licence Number Plate Recognition.pdf Download (2MB) | Preview

Abstract

In the context of rising traffic violations and the need for efficient traffic management, this study explores the application of CNNin the recognition of licence plates to identify traffic violators in Indonesia. Traditional traffic enforcement methods are labour-intensive andprone to human error, necessitating a more automated and reliable approach. This research aims to enhance the accuracy and efficiency oflicense plate recognition (LPR) systems. The proposed system involves capturing vehicle images from the Roboflow Universe collected in theMalang area for use. We also use a CNN model to recognize and extract the alphanumeric characters from the plates. The CNN architecture isdesigned and trained on a comprehensive dataset of Indonesian licence plates, taking into account the unique characteristics and variations inplate designs specific to the region. The research we are doing is detecting number plates to reduce traffic violations. The method used fordetection is the CNN method. The datasets used are primary and secondary. The precision, recall, and F1 score metrics further validate thesystem's reliability and robustness in real-world traffic scenarios. The implementation of this CNN-based LPR system promises a substantialimprovement in monitoring and penalizing traffic violators, contributing to better traffic law enforcement and road safety in Indonesia. Theaccuracy 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 F1score. The accuracy for LPR is 81.20 for precision, 87.37 for recall, and 83.56 for F1 score

Actions (login required)

View Item