Enhanced license plate recognition using deep learning and block-based approach

Yıl 2024, Sayı: 058, 57 - 82, 29.09.2024

Gülistan Arslan , Fırat Aydemir , Seyfullah Arslan

https://doi.org/10.59313/jsr-a.1505302

Öz

This study investigates the effectiveness of current deep learning techniques in license plate detection and makes essential contributions. Instead of classifying the characters on Turkish license plates with a single classifier, the characters are divided into blocks of numbers and letters using various image processing techniques, and a separate classifier is used for each block. The proposed approach improves character classification accuracy and license plate recognition accuracy. This approach eliminated the possibility of misclassifying similar letters and numbers and improved the character classification accuracy from 95.9% to 99.6%. In addition, a new character feature dataset was created, and a deep learning model was trained on this dataset. Integrating this model into the system increased the classification accuracy to 99.7%. The YOLOv8 object detection model, trained using CUDA technology, achieved a mAP of 98.9%. The overall accuracy of the whole system in license plate recognition reached 97.3%. This study proves the effectiveness of current deep learning methods and the proposed block-based character recognition approach in license plate recognition.

Anahtar Kelimeler

License plate recognition, Image processing, Convolutional neural networks, Object detection, Deep learning

Destekleyen Kurum

Kütahya Dumlupınar Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü

Proje Numarası

2022-14

Kaynakça

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