摘要
针对雾霾天气下成像设备获取的图像质量较低导致交通标志难以识别这一现象,笔者提出了先去除雾霾后进行识别的办法。对雾霾图像首先通过深度学习算法IRCNN进行去雾霾处理,然后提出一种多通道卷积神经网络(Multi-channel CNN)模型对去雾霾后的图像进行识别。研究结果表明:IRCNN方法可有效去除雾霾,Multi-channel CNN模型识别效果好,设计的Multi-channel CNN模型的识别率在本次实验的数据集上达到100%,具有很好的泛化性和适应性。
In view of the phenomenon that the image quality acquired by imaging equipment was low in haze weather,which caused difficulty to identify traffic signs,the method of first removing haze and then identifying was proposed.Firstly,the haze image was processed by deep learning algorithm IRCNN,and then a multi-channel convolutional neural network(multi-channel CNN)model was proposed to recognize the image after haze removal.The research results show that IRCNN method can effectively remove haze,and the multi-channel CNN model has a good recognition effect.The recognition rate of the designed multi-channel CNN model reaches 100%on the data set of this experiment,which has good generalization and adaptability.
作者
陈秀新
叶洋
于重重
张雪
CHEN Xiuxin;YE Yang;YU Chongchong;ZHANG Xue(Beijing Key Laboratory of Big Data Technology for Food Safety,Beijing Technology and Business University,Beijing 100048,China)
出处
《重庆交通大学学报（自然科学版）》
CAS
CSCD
北大核心
2020年第12期1-5,12,共6页
Journal of Chongqing Jiaotong University(Natural Science)
基金
国家重点研发计划专项项目(2018YFC0807903)。
关键词
交通工程
智能交通
去雾霾
交通标志识别
IRCNN
多通道卷积神经网络
traffic engineering
intelligent transportation
haze removing
identification of traffic signs
IRCNN
multi-channel CNN
