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

摘要/Abstract

摘要：

交通标志识别对于自动驾驶和辅助驾驶系统中非常重要,但大多数相关研究仅局限在白天场景的识别,若用于夜间,光强差异太大会导致识别准确率显著下降。为解决该问题,文中提出了光强分类模型,可根据光强强度划分场景识别标志牌,保证夜间较高的识别率。该模型通过KNN和SVM构造邻接矩阵和训练特征向量来判断分场景处理出的ROI,从而确定具体标志牌种类。实验证明,该模型在不同环境下识别准确率高达98.1%。

关键词: 多阀值, 二进制图, 感兴趣区域, 支持向量机, 对数极坐标, 图像匹配

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

中图分类号:

TP317.4

严茂森,陈家琪. 一种多环境适用的交通标志识别模型[J]. 电子科技, 2019, 32(4): 68-72.

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

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	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%