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
[1] Salti S, Petrelli A, Tombari F, et al.Traffic sign detection via interest region extraction[J]. Pattern Recognition, 2015, 48(4): 1039-1049.
[2] Gómez-Moreno H, Maldonado-Bascón S, Gil-Jiménez P, et al.Goal evaluation of segmentation algorithms for traffic sign recognition[J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(4): 917-930.
[3] Shadeed W G,Abu-Al-Nadi D I,Mismar M J. Road traffic sign detection in color images[C]∥IEEE International Conference on Electronics, Circuits and Systems, Sharjah, United Arab Emirates, 2003:890-893.
[4] Ruta A, Li Y, Liu X.Real-time traffic sign recognition from video by class-specific discriminative features[J]. Pattern Recognition, 2010, 43(1):416-430.
[5] Li H, Sun F,Liu L, et al.A novel traffic sign detection method via color segmentation and robust shape matching[J]. Neurocomputing, 2015, 169: 77-88.
[6] Loy G, Barnes N.Fast shape-based road sign detection for a driver assistance system[C]∥IEEE International Conference on Intelligent Robots and Systems, Sendai, Japan, 2004:70-75.
[7] Barnes N, Zelinsky A, Fletcher L S.Real-time speed sign detection using the radial symmetry detector[J]. IEEE Transactions on Intelligent Transportation Systems, 2008, 9(2):322-332.
[8] Chen T, Lu S.Accurate and efficient traffic sign detection using discriminative adaboost and support vector regression[J]. IEEE Transactions on Vehicular Technology, 2015, 65(6): 4006-4015.
[9] Wu Y, Liu Y, Li J, et al.Traffic sign detection based on convolutional neural networks[C]∥IEEE International Joint Conference on Neural Networks, Dallas, TX, USA, 2013:1-7.
[10] Qian R, Zhang B, Yue Y, et al.Robust Chinese traffic sign detection and recognition with deep convolutional neural network[C]∥IEEE International Conference on Natural Computation, Zhangjiajie, China, 2015:791-796.
[11] Zang D, Zhang J, Zhang D, et al.Traffic sign detection based on cascaded convolutional neural networks[C]∥IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Shanghai, China, 2016:201-206.
[12] Farhadi A, Endres I, Hoiem D, et al.Describing objects by their attributes[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Miami, Florida, USA, 2009: 1778-1785.
[13] Berg T L, Berg A C, Shih J.Automatic attribute discovery and characterization from noisy web data[C]∥European Conference on Computer Vision, Heraklion, Crete, Greece, 2010:663-676.
[14] Matas J, Chum O, Urban M, et al.Robust wide-baseline stereo from maximally stable extremal regions[J]. Image and Vision Computing, 2004, 22(10):761-767.
[15] Krizhevsky A, Sutskever I, Hinton G E.Imagenet classification with deep convolutional neural networks[C]∥Advances in Neural Information Processing Systems, Lake Tahoe, Nevada, USA, 2012:1097-1105.
[16] Farabet C, Couprie C, Najman L, et al.Learning hierarchical features for scene labeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1915-1929.
[17] Ciresan D, Giusti A, Gambardella L M, et al.Deep neural networks segment neuronal membranes in electron microscopy images[C]∥Advances in neural information processing systems, Lake Tahoe, Nevada, USA, 2012: 2843-2851.
[18] Sermanet P, Eigen D, Zhang X, et al.Overfeat:integrated recognition, localization and detection using convolutional networks[J].arXiv preprint arXiv:1312.6229, 2013.
[19] Girshick R, Donahue J, Darrell T, et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014:580-587.
[20] Sharif R A, Azizpour H, Sullivan J, et al.CNN features off-the-shelf: an astounding baseline for recognition[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014:806-813.
[21] Larsson F, Felsberg M.Using Fourier descriptors and spatial models for traffic sign recognition[C]∥Scandinavian Conference on Image Analysis, Ystad, Sweden, 2011: 238-249.
[22] Houben S, Stallkamp J, Salmen J, et al.Detection of traffic signs in real-world images: the German traffic sign detection benchmark[C]∥IEEE International Joint Conference on Neural Networks, Dallas, TX, USA, 2013:1-8.
[23] Uijlings J R R, van de Sande K E A, Gevers T, et al. Selective search for object recognition[J]. International Journal of Computer Vision, 2013, 104(2): 154-171.
[24] Mathias M, Timofte R, Benenson R, et al.Traffic sign recognition-how far are we from the solution?[C]∥IEEE International Joint Conference on Neural Networks, Dallas, TX, USA, 2013:1-8.
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