YOLO V8 Algoritması ile Otomatik Plaka Tanıma ve Görselleştirme Sistemi

Year 2025, , 1 - 10, 31.01.2025

Esma Serttaş , Fatih Gül

https://doi.org/10.17671/gazibtd.1506041

Abstract

Bu çalışma ile, belirli bir mesafeye yerleştirilen bir kamera ile YOLO (You Only Look Once) V8 algoritmasını kullanarak aracın üzerindeki plakayı otomatik olarak tanıyan ve görselleştiren bir sistem tasarlanmıştır. YOLO V8, gelişmiş bilgisayarlı görü yeteneklerine sahip olmakla birlikte doğrudan plaka tanıma modeli içermemektedir. Bu çalışma ile güvenlik önlemleri gerektiren alanlarda insan gücünü ve maliyeti en aza indirerek verimli şekilde kullanılabilir bir model önerilmiştir. Plaka veri seti, bilgisayarlı görü modeli ortamı Roboflow kullanılarak oluşturulmuş ve yapay sinir ağı eğitim modeli geliştirilmiştir. Python programlama dili kullanılarak YOLO V8 algoritması ile yapay sinir ağı modeli Karayolları Trafik Yönetmeliğine uygun TR plakalar ile eğitilerek plaka tanıma işlemleri gerçekleştirilmiştir. Geliştirilen bu sistemde, açık kaynaklı kütüphaneler olan OpenCV, Time, Random, Numpy, Ultralytics ve EasyOCR kullanılmıştır. Kullanıcı arayüzü için Tkinter kullanılarak plaka tanıma sonuçları görselleştirilmiştir. Sistem tam karşıdan, sağ ve sol yönde 30° içerisinde kalacak şekilde farklı açılardan alınan görüntüler üzerinde test edilmiş ve yüksek doğruluk oranları (%99 @ 25 Epok) elde edilmiştir. Bu çalışma, trafik yönetimi, otopark sistemleri ve güvenlik uygulamaları gibi çeşitli alanlarda mevcut YOLOV8 tabanlı uygulamalara entegre edilebilir bir çözüm yöntemi önermektedir.

Keywords

Plaka Tanıma Sistemi, Görüntü İşleme, YOLOv8, Yapay sinir ağları, Bilgisayarlı görü

Automatic License Plate Recognition and Visualization System with YOLO V8 Algorithm

Abstract

The aim of this study is to develop a system that automatically recognizes and visualize the license plate on the vehicle using the YOLO (You Only Look Once) V8 algorithm with a camera placed at a certain distance. Although YOLO V8 has advanced computer vision capabilities, it does not have a direct license plate recognition model. With this study, a model which can be used efficiently by minimizing manpower and cost in areas that require security measures, was offered. The license plate dataset was developed using the computer vision model environment Roboflow and an artificial neural network training model was created. The license plate recognition operations have been performed by training a neural network model using the YOLO V8 algorithm in Python with TR plates in accordance with the Highway Traffic Regulation. In this developed system, open source OpenCV, Time, Random, Numpy, Ultralytics and EasyOCR libraries were used. By using Tkinter for the user interface, license plate recognition results were visualized. The system was tested on images taken from different angles within 30° from the front, right and left, and high accuracy rates (99% @ 25 Epoch) were obtained. This study offers practical solutions in various fields such as traffic management, parking systems and security applications that can be integrated into current YOLOV8 based applications.

Keywords

license plate recognition system, image processing, YOLOV8, computer vision, artificial neural networks

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