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首页> 外文期刊>International Journal of Innovative Computing Information and Control >ROAD SIGN CLASSIFICATION SYSTEM USING CASCADE CONVOLUTIONAL NEURAL NETWORK
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ROAD SIGN CLASSIFICATION SYSTEM USING CASCADE CONVOLUTIONAL NEURAL NETWORK

机译：级联卷积神经网络的道路标志分类系统

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摘要

We proposed a road sign classification system using C-CNN (cascade con-volutional neural network) classifier. The cascade configuration is designed so that the classifier can easily converge with the data. Our system consists of six stages of Network in Network (NiN) architecture based CNN classifier. The data augmentation method is used to enrich the training and testing dataset which also tests the robustness of our system. Our Japan road sign dataset consists of ten classes with 7,500 examples for each class. Each image cropped from real street images is taken by the camera attached to the top of the car. From the experiments, our system is more efficient compared with bag-of-features method. The execution time of our system is less than 20 ms using appropriate hardware configuration, which is suitable for real-time application approaches like an autonomous car or driver assistance system.
机译：我们提出了一种使用C-CNN（级联卷积神经网络）分类器的路标分类系统。设计级联配置，以便分类器可以轻松地与数据收敛。我们的系统包含六个阶段的基于CNN分类器的Network in Network（NiN）体系结构。数据扩充方法用于丰富训练和测试数据集，这也测试了我们系统的鲁棒性。我们的日本路标数据集包含十个类别，每个类别有7,500个示例。从真实街道图像中裁剪出来的每个图像都由安装在汽车顶部的摄像头拍摄。从实验来看，我们的系统比功能袋方法更有效。使用适当的硬件配置，我们的系统的执行时间少于20毫秒，适用于实时应用方法，例如自动驾驶汽车或驾驶员辅助系统。

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  • 作者

    REZA FUAD RACHMADI; YOSHINORI KOMOKATA; KEIICHI UCHIMURA; GOU KOUTAKI;

  • 作者单位

    Graduate School of Science and Technology Kumamoto University Kumamoto-shi, Chuo-ku, Kurokami 2-39-1, Kumamoto 860-8555, Japan,Department of Multimedia and Network Engineering Institut Teknologi Sepuluh Nopember Keputih, Sukolilo, Surabaya 60111, Indonesia;

    Graduate School of Science and Technology Kumamoto University Kumamoto-shi, Chuo-ku, Kurokami 2-39-1, Kumamoto 860-8555, Japan;

    Graduate School of Science and Technology Kumamoto University Kumamoto-shi, Chuo-ku, Kurokami 2-39-1, Kumamoto 860-8555, Japan;

    Graduate School of Science and Technology Kumamoto University Kumamoto-shi, Chuo-ku, Kurokami 2-39-1, Kumamoto 860-8555, Japan;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Road sign classification; C-CNN; Data augmentation; NiN architecture;

    机译：道路标志分类;C-CNN;数据扩充;iN架构;

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