Detection and Classification of Small Traffic Signs Based on Cascade Network  

作　　者：ZHANG Shufang WANG Qinyu ZHU Tong LIU Yuhong 

机构地区：[1]School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China [2]Department of Computer Science and Engineering,Santa Clara University,Santa Clara 95053,USA

出　　处：《Chinese Journal of Electronics》2021年第4期719-726,共8页电子学报（英文版）

摘　　要：Research on the traffic sign detection is significant for driverless technology,which provides useful navigation information.Existing object detection methods are only applicable to large-size objects or small-scale specific types of traffic signs,and the performance of detecting traffic signs in street views is not adequate.In this regard,we propose a method to detect and classify small traffic signs by constructing a cascaded network.Specifically,the RetinaNet network is adopted firstly to integrate multi-layer information to identify small traffic signs in traffic scene images.The focal loss function is used to balance the biased distribution of traffic sign categories.Then,a two-class network is cascaded after the RetinaNet,which helps identify valid traffic signs from the first-stage prediction results.Experiments show that our cascaded network structure could achieve the balance of different categories of predictions and an improvement in precision and recall.

关 键 词：Feature extraction Image classification Object detection Transportation industry 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] U463.6[自动化与计算机技术—计算机科学与技术]