• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Natural Computation;International Conference on Fuzzy Systems and Knowledge Discovery >Robust chinese traffic sign detection and recognition with deep convolutional neural network
【24h】

Robust chinese traffic sign detection and recognition with deep convolutional neural network

机译：深度卷积神经网络的鲁棒中国交通标志检测与识别

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Detection and recognition of traffic sign, including various road signs and text, play an important role in autonomous driving, mappingavigation and traffic safety. In this paper, we proposed a traffic sign detection and recognition system by applying deep convolutional neural network (CNN), which demonstrates high performance with regard to detection rate and recognition accuracy. Compared with other published methods which are usually limited to a predefined set of traffic signs, our proposed system is more comprehensive as our target includes traffic signs, digits, English letters and Chinese characters. The system is based on a multi-task CNN trained to acquire effective features for the localization and classification of different traffic signs and texts. In addition to the public benchmarking datasets, the proposed approach has also been successfully evaluated on a field-captured Chinese traffic sign dataset, with performance confirming its robustness and suitability to real-world applications.
机译：交通标志的检测和识别，包括各种道路标志和文字，在自动驾驶，地图/导航和交通安全中起着重要作用。在本文中，我们通过应用深度卷积神经网络（CNN）提出了一种交通标志检测和识别系统，该系统在检测率和识别精度方面表现出很高的性能。与通常限于一组预定义交通标志的其他已发布方法相比，我们提出的系统更加全面，因为我们的目标包括交通标志，数字，英文字母和汉字。该系统基于经过训练的多任务CNN，以获取有效的功能，以对不同的交通标志和文本进行定位和分类。除了公开的基准数据集之外，该方法还已经在现场捕获的中国交通标志数据集上成功进行了评估，其性能证实了其鲁棒性和对实际应用的适用性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号