现代电子技术Issue(13)：101-106,6.
应用深层卷积神经网络的交通标志识别
Traffic signs recognition applying with deep-layer convolution neural network
摘要
Abstract
In actual traffic circumstance,the image quality of collected traffic signs is worse due to motion blur,back⁃ground disturbances,weather conditions,and shooting angle. The higher requirements are proposed for accuracy,robustness and in real⁃time of traffic signs auto⁃recognition. In this situation,the traffic signs recognition method based on deep⁃layer convo⁃lution neural network is presented. The method adopts supervised learning model of deep⁃layer convolution neural network. Taking the collected traffic sign images through binarization as the inputs. The inputs are conducted multilayer process of convolution and pooling⁃sampling,to simulate hierarchical structure of human brain perception visual signals,and extract the characteristics of the traffic sign images automatically. Traffic signs recognition is realized by using a full connected network. The experimental results show that the method can extract the characteristics of traffic signs automatically by using deep learning ability of the con⁃volution neural network. The method has good generalization ability and adaptive range. By using this method,the traditional ar⁃tificial feature extraction is avoided,and the efficiency of traffic signs recognition is improved effectively.关键词
交通标志/识别/卷积神经网络/深度学习Key words
traffic sign/recognition/convolution neural network/deep learning分类
信息技术与安全科学引用本文复制引用
黄琳,张尤赛..应用深层卷积神经网络的交通标志识别[J].现代电子技术,2015,(13):101-106,6.基金项目
国家自然科学基金面上项目 （）
