张永雄,王亮明,李东

• 1:华南理工大学软件学院
• 2:广州工商学院
• 3:华南理工大学计算机科学与工程学院

摘要(Abstract)：

为了解决当前交通标志种类繁多和所处环境多变,导致智能识别正确率不高的问题,提出基于多示例深度学习的交通标志识别算法。根据样本图像块与其对应的标签设计一个包含颜色、几何、区域特征的训练集,得到样本特征与标签的对应规律;根据权重修正反馈,推导包与标签的逻辑关系,建立多示例训练学习算子,准确分类交通标志。进行训练集损失函数计算,通过最优分类器响应减少训练数据损失。最后,基于大数据样本驱动形成背景约束,从而去除示例中模棱两可的训练数据,完成交通标志的准确识别。基于QT平台,开发相应的识别软件。实验测试结果显示,与当前交通标志识别技术相比,所提算法拥有更高的识别正确性与鲁棒性,且对各类交通标志具有较高的识别准确率,在智能汽车、自动交通监控等领域具有一定的应用价值。

关键词(KeyWords)： 交通标志识别;损失函数优化;训练集;多示例;深度学习;背景约束

Abstract:

Keywords:

基金项目(Foundation): 家庭信息平台的产业化推广;2013年广东省教育部产学研重大成果转化项目(2013B090200055)~~

作者(Author): 张永雄,王亮明,李东

DOI: 10.16652/j.issn.1004-373x.2018.15.030

参考文献(References)：

• [1]谢锦,蔡自兴,邓海涛,等.基于图像不变特征深度学习的交通标志分类[J].计算机辅助设计与图形学学报,2017,29(4):632-640.XIE Jin,CAI Zixing,DENG Haitao,et al.Classification of traffic signs based on image invariant feature depth learning[J].Journal of computer aided design and computer graphics,2017,29(4):632-640.
• [2]KR?áK E,TOTH?.Traffic sign recognition and localization for databases of traffic signs[J].Acta electrotechnica et informatica,2011,11(4):31-35.
• [3]王晓斌,黄金杰,刘文举.基于优化卷积神经网络结构的交通标志识别[J].计算机应用,2017,37(2):530-534.WANG Xiaobin,HUANG Jinjie,LIU Wenju.Traffic sign recognition based on optimized convolutional neural network[J].Computer applications,2017,37(2):530-534.
• [4]戈侠,于凤芹,陈莹.基于分块自适应融合特征的交通标志识别[J].计算机工程与应用,2017,53(3):188-192.GE Xia,YU Fengqin,CHEN Ying.Traffic sign recognition based on block adaptive fusion feature[J].Computer engineering and applications,2017,53(3):188-192.140
• [5]ZHOU Shusen,ZOU Hailin,LIU Chanjuan.Deep extractive networks for supervised learning[J].Optik:international journal for light and electron optics,2016,127(20):9008-9019.
• [6]FARIA A W C,COELHO F G F,SILVA A M.A new approach for multiple instance learning based on positive instance selection and kernel density estimation[J].Engineering applications of artificial intelligence,2017,59(12):196-204.
• [7]刘占文,赵祥模,李强,等.基于图模型与卷积神经网络的交通标志识别方法[J].交通运输工程学报,2016,16(5):122-131.LIU Zhanwen,ZHAO Xiangmo,LI Qiang,et al.A traffic sign recognition method based on graph model and convolutional neural network[J].Journal of traffic and transportation engineering,2016,16(5):122-131.
• [8]ZHONG Jingjing,TSE P W,WANG Dong.Novel Bayesian inference on optimal parameters of support vector machines and its application to industrial survey data classification[J].Neurocomputing,2016,211:159-171.
• [9]YU Yongtao.Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data[J].ISPRS journal of photogrammetry and remote sensing,2016,11(7):36-39.
• [10]OUERHANI Y.Advanced driver assistance system:road sign identification using VIAPIX system and a correlation technique[J].Optics and lasers in engineering,2016,12(2):71-76.