Aif Umar Nawawi, Arya Muhammad Dendyana, Galuh Yudha Prastyo, Silmi Ath Thahirah Al Azhima, Nurul Fahmi Arief Hakim, Azwar Mudzakkir Ridwan
The technology known as Automatic License Plate Recognition (ALPR) originated from the application of computer vision, a subfield of artificial intelligence systems. The purpose of developing this ALPR system was to verify the tax status and comprehensive vehicle information in the West Java region of Indonesia. The objective of this study was to instill in each motorist a sense of traffic order and to assist Indonesia in the implementation of an Intelligent Traffic System (ITS). In order to operate efficiently, the system incorporated in this design is constructed using a variety of Python programming language modules. YOLOv8 (You Only Look Once version 8), EasyOCR, OpenCV, and Selenium are the primary Python modules utilized; additional modules include RegEx (Regular Expression), Time, Numpy, and BytesIO. In this application, number plates are searched for and detected using the dilation method so that EasyOCR can extract every character with minimal error. Additionally, machine learning serves as the foundation for each of the aforementioned procedures and processes. The fundamental computation of machine learning from bespoke data sets is performed in Google Colab. The outcome of this investigation is a system capable of furnishing information pertaining to motor vehicle systems to an individual who has failed to remit vehicle tax. Aside from that, the developed system can detect counterfeit license plates, making it a valuable tool for educating drivers about the importance of remaining vigilant. © 2024 IEEE.
Universitas Pendidikan Indonesia, Dept. of Electrical Eng. Education, Bandung, Indonesia; UIN Sunan Gunung Djati, Dept. of Electrical Engineering, Bandung, Indonesia
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