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

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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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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)

CLC Number: 

  • TN911.73
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