吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (1): 319-329.doi: 10.13229/j.cnki.jdxbgxb20161120

• 论文 • 上一篇    下一篇

基于深度属性学习的交通标志检测

王方石1, 王坚1, 李兵2, 3, 王博2, 3   

  1. 1.北京交通大学 软件学院,北京 100044;
    2.中国科学院 自动化研究所,北京 100190;
    3.中国科学院 模式识别国家重点实验室,北京 100190
  • 收稿日期:2016-10-14 出版日期:2018-02-26 发布日期:2018-02-26
  • 通讯作者: 李兵(1983-),男,副研究员,博士. 研究方向:模式识别.E-mail: bli@nlpr.ia.ac.cn
  • 作者简介:王方石(1969-),女,教授,博士. 研究方向:视觉信息处理.E-mail: fshwang@bjtu.edu.cn
  • 基金资助:
    国家自然科学基金项目(61370038, 61472421)

Deep attribute learning based traffic sign detection

WANG Fang-shi1, WANG Jian1, LI Bing2, 3, WANG Bo2, 3   

  1. 1.School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China;
    2. Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China;
    3.National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2016-10-14 Online:2018-02-26 Published:2018-02-26

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摘要： 为了弥补交通标志底层图像到高层语义之间的鸿沟,本文引入交通标志的形状、颜色、图案内容三种视觉属性,在卷积神经网络(Convolutional neural network, CNN)中加入属性学习(Attribute learning)约束,同时进行交通标志属性学习和分类学习,提出了一种基于深度属性学习的交通标志检测方法。并在公开数据集Sweden traffic sign detection dataset(STSD)和German traffic sign detection dataset(GTSD)上进行的实验结果表明,该方法能够有效地提高交通标志检测的准确率和召回率。

关键词: 信息处理技术, 交通标志检测, 深度属性学习, 卷积神经网络

Abstract: A traffic sign detection method based on deep attribute learning was proposed. To make up the gap between raw image and high level semantics, three visual attributes, including shape, color and pattern, were introduced. Attribute learning was added to train Convolutional Neural Network (CNN), where attribute learning and classification learning were carried out simultaneously. Experimental results on datasets Sweden Traffic Sign Detection Dataset (STSD) and German Traffic Sign Detection Dataset (GTSD) show that the proposed method can effectively improve the precision and recall in terms of traffic sign detection.

Key words: information processing technology, traffic sign detection, deep attribute learning, convolutional neural network(CNN)

中图分类号: 

  • TN911.73
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