标签结合现实场景的交通标志分类检测研究  

Research on classification and detection of traffic signs based on tags combined with real scenes

作　　者：张成 张瑞宾 王曙道 Zhang Cheng;Zhang Ruibin;Wang Shudao(School of Automobile and Traffic Engineering,Guilin University of Aerospace Technology,Guilin 541004,China)

机构地区：[1]桂林航天工业学院汽车与交通工程学院,广西桂林541004

出　　处：《电子技术应用》2022年第3期27-31,36,共6页Application of Electronic Technique

基　　金：广西高校中青年教师基础能力提升项目(2019KY0819)。

摘　　要：交通标志在车辆的安全行驶和自动驾驶中都有着大量的研究。由于交通标志的种类繁多且受各种因素的影响,交通标志的分类检测也是一个具有挑战的难题。为此,提出了一种标签结合现实道路场景的交通标志分类检测方法,该方法分为数据生成部分和目标检测部分。实验结果表明,利用该方法生成训练数据,能够有效地训练深度卷积神经网络,实现现实场景交通标志的分类检测,并且优化的检测模型相比文中提到的模型具有更小的体积和更快的速度。There are a lot of researches on traffic signs in the safe driving and automatic driving of vehicles.Due to the wide variety of traffic signs and the influence of various factors,the classification and detection of traffic signs is also a challenging problem.To this end,a traffic sign classification and detection method combining tags with real road scenes is proposed.The method is divided into a data generation part and a target detection part.Experimental results show that the use of this method to generate training data can effectively train deep convolutional neural networks to achieve classification and detection of traffic signs in real scenes,and the optimized detection model has a smaller size and faster speed than the model mentioned in the article.

关 键 词：交通标志 自动驾驶 数据增强 深度卷积神经网络 检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]