Comparison of Tensorflow Object Detection Networks for Licence Plate Localization
Küçük Resim Yok
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this work, the object detection networks of TensorFlow framework are trained and tested for the automatic license plate localization task. Firstly, a new dataset is prepared for Turkish license plates. The images in the dataset are labeled with two classes which are the car and the license plate. Four different object detection networks were configured to run on Google's Colab environment. These network configurations were the Single Shot MultiBox Detector (SSD) using MobileNet features and Resnet50 features, the Faster Region Convolutional Neural Network (Faster R-CNN) using Inception layers for features, and the Region-based Fully Convolutional Networks (R-FCN) with Resnet101 features. These networks were compared to determine the performance of license plate localization. Different types of input images were used to test the algorithms.
Açıklama
1st Global Power, Energy and Communication Conference (IEEE GPECOM) -- JUN 12-15, 2019 -- Nevsehir, TURKEY
Anahtar Kelimeler
SSD, Faster R-CNN, R-FCN, object detection, license plate localization
Kaynak
2019 Ieee 1st Global Power, Energy and Communication Conference (Gpecom2019)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
