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首页> 外文会议>2019 1st Global Power, Energy and Communication Conference >Comparison of Tensorflow Object Detection Networks for Licence Plate Localization
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Comparison of Tensorflow Object Detection Networks for Licence Plate Localization

机译：用于车牌定位的Tensorflow对象检测网络的比较

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摘要

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. Index Terms-SSD, Faster R-CNN, R-FCN, object detection, license plate localization.
机译：在这项工作中，针对自动车牌定位任务对TensorFlow框架的对象检测网络进行了培训和测试。首先，为土耳其车牌准备了一个新的数据集。数据集中的图像用汽车和车牌两类标记。配置了四个不同的对象检测网络以在Google的Colab环境中运行。这些网络配置包括使用MobileNet功能和Resnet50功能的Single Shot MultiBox Detector（SSD），使用功能的Inception层的Faster Region卷积神经网络（Faster R-CNN）以及基于区域的完全卷积网络（R-FCN）具有Resnet101功能。比较这些网络以确定车牌本地化的性能。使用不同类型的输入图像来测试算法。索引词-SSD，更快的R-CNN，R-FCN，对象检测，车牌定位。

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