Balkan Journal of Electrical and Computer Engineering 2147-284X 2147-284X MUSA YILMAZ 10.17694/bajece.1093158 Artificial Intelligence Yapay Zeka Development of a Counterfeit Vehicle License Plate Detection System by Using Deep Learning https://orcid.org/0000-0002-9183-1568 Ağgül Burak ISTANBUL AYVANSARAY UNIVERSITY, PLATO VOCATIONAL SCHOOL https://orcid.org/0000-0003-4095-6333 Erdemir Gökhan İSTANBUL SABAHATTİN ZAİM ÜNİVERSİTESİ 07 30 2022 10 3 252 257 03 25 2022 05 25 2022 Copyright © 2013, Balkan Journal of Electrical and Computer Engineering 2013 Balkan Journal of Electrical and Computer Engineering

In this study, a deep learning-based counterfeit plate detection system that compares and detects vehicles with the make, model, color, and license plate is designed. As known that the relevant government institutions are responsible for keeping all detailed information about all motor vehicles in their database. All registration details are stored in the database. It is possible to find unregistered vehicles by comparing database records with detected details. In general, vehicles with counterfeit license plates are used in illegal actions. Therefore, it is of great importance to detect them. Generally, license plate recognition systems successfully detect counterfeit license plates that are randomly generated. Security units typically use such systems at toll roads, bridge crossings, parking lot entrances and exits, sites, customs gates, etc. This kind of system only checks the plate is exists or not in the database. But it is unsuccessful if the vehicle uses existing plate numbers such as stolen ones. In this study, the developed system can detect not only vehicles' plate numbers but also make, model, year, and color information by using deep learning. Thus, the system can also detect randomly generated plates and stolen plates that belong to another vehicle.

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