摘要
交通标识分类是交通标识识别系统的基础环节,而交通标识形状识别是交通标识分类的核心部分。对交通标识进行了研究,将交通标识分为禁令标识、警告标识和指示标识3大类分别进行分析,提出了一种利用边缘走势统计特征反映目标形状特征的新算法,并将其与BP神经网络相结合用于交通标识形状的识别。首先利用颜色信息实现交通标识区域分割,随后记录交通标识的边缘走势并统计比例,最后使用BP神经网络进行分类,实现交通标识形状的识别。该算法对不同倾斜角度和不同拍摄角度的交通标识图像均具有很好的识别效果和识别速率。
Traffic sign classification is the basic link of traffic sign recognition system,and traffic sign shape recognition is the core part of traffic sign classification.This paper studies traffic signs and analyzes them into three categories:ban signs,warning signs and instruction signs,respectively.A new algorithm is proposed,which uses the statistical feature of edge trend to reflect the feature of target shape.It is combined with BP neural network to identify the shape of traffic signs.Firstly,the color information is used to realize the segmentation of traffic signs.Secondly,the edge trend of traffic signs is recorded and the proportion is counted.Finally,BP neural network is used for classification to realize the identification of the shape of traffic signs.This method has good recognition effect and speed for traffic sign images with different tilt angles and shooting angles.
作者
邓翔宇
张屹南
杨雅涵
DENG Xiang-yu;ZHANG Yi-nan;YANG Ya-han(College of Physics and Electronic Engineering,Northwest Normal University,Lanzhou 730070,China)
出处
《计算机工程与科学》
CSCD
北大核心
2021年第2期322-328,共7页
Computer Engineering & Science
基金
国家自然科学基金(61961037)。
关键词
形状识别
交通标识分类
边缘走势
方向特征统计
BP神经网络
shape recognition
traffic sign classification
edge trend
directional feature statistics
BP neural network
