基于改进YOLOX的交通标志检测与识别  

Traffic Sign Detection and Recognition Based on Improved YOLOX

作　　者：陈民 吴观茂[1] CHEN Min;WU Guanmao(School of Computer Science and Engineering,Anhui University of Technology,Huainan 232001,China)

机构地区：[1]安徽理工大学计算机科学与工程学院,安徽淮南232001

出　　处：《现代信息科技》2022年第2期101-103,106,共4页Modern Information Technology

基　　金：安徽省自然科学基金项目(1908085MF189)。

摘　　要：现实中交通标志的检测和识别具有环境多变的特点,交通标志长时间暴露在外经常会出现损坏情况,对检测的精度和速度产生较大影响。利用最新的YOLO系列算法——YOLOX,对网络结构的加强特征提取层进行改进,引入OPA-FPN网络,相较于原来的PANet网络,后者精度提升2.2%。在交通标志识别过程,对经典的卷积神经网络模型LeNet-5进行改进,在数据集TT100K中进行实验,相较于其他交通标志识别模型,使用改进的模型可以使识别正确率提升2.31%,识别时间减少了13.02 ms。In reality,the detection and recognition of traffic signs have the characteristics of changeable environment.Traffic signs are often damaged after being exposed for a long time,which has a great impact on the accuracy and speed of detection.Using the latest YOLO series algorithm—YOLOX,the enhanced feature extraction layer of the network structure is improved,and the OPA-FPN network is introduced.Compared with the original PANet network,the accuracy of the latter is improved by 2.2%.In the process of traffic sign recognition,the classical convolutional neural network model LeNet-5 is improved,experiments are carried out in the data set TT100K.Compared with other traffic sign recognition models,using the improved model can improve the recognition accuracy by 2.31%and reduce the recognition time by 13.02 ms.

关 键 词：单步路径聚合网络 YOLO 卷积神经网络 FPN LeNet-5 

分 类 号：TP273.4[自动化与计算机技术—检测技术与自动化装置]