基于LeNet-5卷积神经网络和颜色特征的限速标志识别  

Speed Limit Sign Recognition Based on LeNet-5 CNN and Color Feature

作　　者：王济民 魏怡[1] 周宇 孙傲 刘源升 WANG Ji-min;WEI Yi;ZHOU Yu;SUN Ao;LIU Yuan-sheng(School of Automation,Wuhan University of Technology,Wuhan 430070,China)

机构地区：[1]武汉理工大学自动化学院,武汉430070

出　　处：《计算机科学》2021年第S02期345-350,共6页Computer Science

基　　金：国家自然科学基金(51177114);湖北省技术创新重大专项(2019AAA016)。

摘　　要：限速标志识别是智能驾驶的重要组成部分,文中分析了现有方法存在的问题,为了提高神经网络在中国限速标志上的泛用性和准确率,针对限速标志的检测部分,提出了一种基于颜色空间的新型筛选方法;针对限速标志的识别部分,在现有LeNet-5架构的基础上对神经网络进行了改进,并将德国交通标志数据集(GTSRB)和清华交通标志数据集(TT100K)中限速标志数据融合,经过数据扩增后制作成新的数据集送入神经网络来训练模型。通过多次超参数优化,采用swish激活函数,在测试集上得到的最优识别准确率为99.62%,且模型抗干扰能力强,具有较强的实用性能。Speed limit sign recognition is an important part of intelligent driving.This research analyzes the problems of existing methods.In order to improve the versatility and accuracy of neural networks on Chinese speed limit signs,in the detection part of speed limit signs,a new screening method based on color space is proposed.In the recognition part of the speed limit sign,the neural network is improved on the basis of the existing LeNet-5 architecture.By fusing the German traffic sign data set(GTSRB)and Tsinghua traffic sign data set(TT100K),a new data set is made and sent to the neural network to train the model after data amplification.Using swish activation function,the optimal recognition accuracy rate obtained on the test set is 99.62%,and the model has strong anti-interference ability and strong practical performance.

关 键 词：卷积神经网络 限速标志识别 颜色空间 数据增广 高斯圆检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]