基于卷积神经网络的铁轨路牌识别方法  

An Identification Method of High-speed Railway Sign Based on Convolutional Neural Network

作　　者：孟琭[1] 孙霄宇 赵滨 李楠[3] MENG Lu;SUN Xiao-Yu;ZHAO Bin;LI Nan(College of Information Science and Engineering,Northeastern University,Shenyang 110819;UUValue Technology Co.,Ltd,Tianjin 300452;Shenyang Product Quality Supervision and Inspection Institute,Shenyang 110022)

机构地区：[1]东北大学信息科学与工程学院,沈阳110819 [2]友和利德科技有限公司,天津300452 [3]沈阳产品质量监督检验院,沈阳110022

出　　处：《自动化学报》2020年第3期518-530,共13页Acta Automatica Sinica

摘　　要：轨道交通在我国综合交通运输体系中占有重要的地位,随着人工智能的发展,智能感知轨道交通周围环境的信息也变得越来越引人注目.本文结合深度学习与图像处理的方法,设计并实现了一种基于卷积神经网络的高铁轨道周边路牌数字识别的智能系统,该系统通过在高铁驾驶室内安装摄像头的方式采集运行前方的视频,并通过目标识别、语义分割等深度学习算法自动定位并识别路牌内的数字,从而解决了之前人工处理的繁琐和低效率.本算法整体系统由三个子模块构成,分别为目标检测模块、语义分割模块以及数字识别模块,其中目标检测模块基于SSD(Single shot MultiBox dector)模型,并对其进行了改进,使其更适用于轨道交通中的小目标识别;语义分割模块使用了全卷积的方式,对目标检测的结果进一步处理,准确得到路牌中的数字区域;数字识别模块的设计参考了著名的识别MNIST数据集的手写体识别系统,并针对路牌中数字的特点做了相应的改进,实现了对每个数字的准确识别.实验结果表明,本系统可适应白天、夜间情况下轨道交通的路况,识别的综合准确率为80.45%,其中,白天的平均识别准确率为87.98%,夜间的平均识别准确率为72.92%.Rail transit plays an important role in China's comprehensive transportation system.Intelligent perception of environmental information around rail traffic is also becoming more and more attractive.Combining the methods of deep learning and image processing,the paper designs and implements an intelligent system that is based on convolutional neural network for identification of rail digital signs around high-speed rail.The system not only collects videos by installing a camera in the high-speed rail cab but also automatically locates and identifies the numbers in the railway sign by the depth learning algorithm such as object detection and semantic segmentation,which can solve the cumbersome and inefficient manual processing.The total system of the algorithm consists of three sub-modules:the object detection module is based on the single shot MultiBox dector(SSD)model and improves it to be more suitable to detect the small target in the rail transit;the semantic segmentation module uses the full convolution method to further process the result of the object detection module and then get accurate digital region in the rail sign;the design of the digital identification module referred to the famous handwriting recognition system that recognizes the MNIST dataset.Besides,it improved the characteristics of the numbers in the railway signs and achieved the accurate identification of each number.The experimental results show that the system can adapt to the conditions of various rail transits,including:day and night.The comprehensive accuracy of recognition is 80.45%.Furthermore,the average accuracy of the daytime is 87.98%,and the average accuracy of the night is 72.92%.

关 键 词：智能轨道交通 高铁路牌识别 深度学习 图像处理 目标检测 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术] U29-39[交通运输工程—交通运输规划与管理]