一种多环境适用的交通标志识别模型

doi:10.16180/j.cnki.issn1007-7820.2019.04.015

Abstract

Abstract:

Traffic sign recognition plays an important role in autopilot and assisted driving systems. However, most related studies were limited to the identification of daylight scenarios. If these models were applied in night, the difference of light intensity would lead to a significant decrease in recognition accuracy. In order to solve this problem, this paper proposed a light intensity classification model, which used different methods to identify the signboard according to the difference of light intensity to ensure excellent recognition rates in night. Firstly, the model divided the scene and processed the ROI by different methods. And then, eigenvectors were trained by KNN and SVM. Finally, the trained eigenvectors could identify the type of a sign. Experiments proved that the accuracy rate of this model in different environments was as high as 98.1%.

Key words: multiple thresholding, binary images, ROI, SVM, log polar, NCC

CLC Number:

TP317.4

YAN Maosen,CHEN Jiaqi. A Multi-environmental Traffic Sign Recognition Model[J].Electronic Science and Technology, 2019, 32(4): 68-72.

Figures/Tables 6

Figure 1.

Table 1

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Table 3

Figure 3.

References 17

[1]	Gudigar A, Chokkadi S, Raghavendra U . A review on automatic detection and recognition of traffic sign[J]. Multimedia Tools and Applications, 2016,75(1):333-364. doi: 10.1007/s11042-014-2293-7
[2]	Zaklouta F, Stanciulescu B . Real-time traffic sign recognition in three stages[J]. Robotics and Autonomous Systems, 2014,62(1):16-24. doi: 10.1016/j.robot.2012.07.019
[3]	李志森, 陈晓荣 .基于 Halcon 的银行卡卡号识别[J]. 电子科技, 2017,30(9):56-59. doi: 10.16180/j.cnki.issn1007-7820.2017.09.016
	Li Zhisen, Chen Xiaorong . Research on image analysis of bank card based on Halcon software[J]. Electronic Science and Technology, 2017,30(9):56-59. doi: 10.16180/j.cnki.issn1007-7820.2017.09.016
[4]	Pazhoumanddar H, Yaghoobi M . A new approach in road sign recognition based on fast fractal coding[J]. Neural Computing and Applications, 2013,22(3-4):615-625. doi: 10.1007/s00521-011-0718-z
[5]	Souani C, Faiedh H, Besbes K . Efficient algorithm for automatic road sign recognition and its hardware implementation[J]. Journal of Realtime Image Processing, 2014,9(1):79-93. doi: 10.1007/s11554-013-0348-z
[6]	吴迪, 刘伟峰, 胡胜 , 等. 基于Lab空间的K均值聚类彩色图像分割[J]. 电子科技, 2017,30(10):29-32. doi: 10.16180/j.cnki.issn1007-7820.2017.10.009
	Wu Di, Liu Weifeng, Hu Sheng , et al. Color image segmentation using K-mean clustering based on Lab space[J]. Electronic Science and Technology, 2017,30(10):29-32. doi: 10.16180/j.cnki.issn1007-7820.2017.10.009
[7]	李健, 曲怀敬, 王美平, 等 .基于 PCA 和广义高斯建模的多聚焦图像融合[J]. 计算机工程与应用, 2017,53(5):216-221. doi: 10.3778/j.issn.1002-8331.1506-0023
	Li Jian, Qu Huanjin, Wang Meiping , et al. Multi-focus image fusion based on PCA and generalized Gaussian modeling[J]. Computer Engineering and Applications, 2017,53(5):216-221. doi: 10.3778/j.issn.1002-8331.1506-0023
[8]	李斌, 程丹, 李星 . 基于Direct LDA 的相关向量机遥感图像分类[J]. 信息技术, 2017,41(4):17-20. doi: 10.13274/j.cnki.hdzj.2017.04.005
	Li Bin, Chen Dan, Li Xing . Relevant vector machine classification of hyperspectral image based on direct linear discriminant analysis[J]. Information Technology, 2017,41(4):17-20. doi: 10.13274/j.cnki.hdzj.2017.04.005
[9]	Gamec J, Urdzík D, Gamcová M . Algorithm of traffic signs recognition based on the rapid transform[J]. Open Computer Science, 2012,2(3):236-244. doi: 10.2478/s13537-012-0019-3
[10]	姜杰, 刘哲, 吕林涛 . 局部线性嵌入的快速单幅图像超分辨率技术[J]. 红外技术, 2018,40(1):39-46.
	Jiang Jie, Liu Zhe, Lv Lintao . Fast single-image super resolution technology based on local linear embedding[J]. Infrared Technology, 2018,40(1):39-46.
[11]	Liu H, Liu Y, Sun F . Traffic sign recognition using group sparse coding[J]. Information Sciences, 2014,266:75-89. doi: 10.1016/j.ins.2014.01.010
[12]	Gudigar A, Chokkadi S, Raghavendra U , et al. Multiple thresholding and subspace based approach for detection and recognition of traffic sign[J]. Multimedia Tools and Applications, 2017,76(5):6973-6991. doi: 10.1007/s11042-016-3321-6
[13]	Raghavendra U, Makkithaya K, Karunakar A K . Anchor-diagonal-based shape adaptive local support region for efficient stereo matching[J]. Signal, Image and Video Processing, 2015,9(4):893-901. doi: 10.1007/s11760-013-0524-4
[14]	王斌, 常发亮, 刘春生 .基于 MSER 和 SVM 的快速交通标志检测[J]. 光电子·激光, 2016,27(6):625-632.
	Wang Bin, Chang Faliang, Liu Chunsheng . Rapid traffic sign detection based on MSER and SVM[J]. Journal of Optoelectronics·Laser, 2016,27(6):625-632.
[15]	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. doi: 10.3778/j.issn.1002-8331.1505-0005
	Ge Xia, Yu Fengqin, Chen Ying . Traffic sign recognition based on blocking self-adaptation fusion features[J]. Comuter Engineering and Applications, 2017,53(3):188-192. doi: 10.3778/j.issn.1002-8331.1505-0005
[16]	Houben S, Stallkamp J, Salmen J, et al. Detection of traffic signs in real-world images: the german traffic sign detection benchmark [C].Dallas:International Joint Conference on Neural Networks,IEEE, 2013.
[17]	徐彬森, 魏元周, 毛光明 , 等. 交通标志识别算法模型的研究与实现[J]. 软件, 2017,38(11):74-81. doi: 10.3969/j.issn.1003-6970.2017.11.015
	Xu Binsen, Wei Yuanzhou, Mao Guangming , et al. Research and implementation of traffic sign recognition algorithm[J]. Software, 2017,38(11):74-81. doi: 10.3969/j.issn.1003-6970.2017.11.015

	第一组	第二组	第三组	第四组
文献[14]精准率	92.37%	98.24%	80.44%	99.02%
文献[15]精准率	89.96%	97.05%	74.19%	98.21%
本模型精准率	96.28%	99.67%	92.31%	100%
本模型召回率	99.66%	92.90%	69.55%	100%
标志总数	297	324	243	207
本模型检测到的标志数	296	301	169	207
本模型未检测到的标志数	1	23	74	0
本模型检测错误的标志数	11	1	13	0

	方法	公有数据集准确率	所有数据集准确率
文献[17]	单场景	-	-
神经网络	95.1%	94.8%	-
文献[14]	单场景 SVM	96.3%	95.9%
本模型	分场景 SVM	98.2%	98.1%

A Multi-environmental Traffic Sign Recognition Model

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