摘要
交通标志的识别对于自动驾驶与智能导航具有重要意义,针对已有深度学习网络识别率不高的问题,提出一种基于ConvNeXt网络模型的交通标志智能识别算法。该网络以纯粹的CNN模型为特点,具有更优的图像分类及检测分割任务的性能。文中使用GTSRB数据集进行实验,与MobileNet、ResNet等网络进行对比测试,测试结果表明,ConvNeXt网络收敛速度最快并且稳定,最终交通标志的识别准确率达99%以上。实验结果表明,该算法准确率高,具有一定的工程应用意义。
Traffic sign recognition is of great significance for automatic driving and intelligent navigation,and an intelligent recognition algorithm of traffic signs based on ConvNeXt network model is proposed to solve the problem that the recognition rate of existing deep learning networks is not high.The network features a pure CNN model with better performance for image classification and detection segmentation tasks.In this paper,GTSRB data sets are used for experiments and compared with MobileNet,ResNet,and other networks.The test results show that the ConvNeXt network has the fastest convergence speed and is stable,and the final traffic sign recognition accuracy rate reaches over 99%.Experimental results show that the algorithm has high accuracy and has certain engineering application significance.
作者
李伟娟
千凯琦
付昱
伍晨俊
刘保山
LI Weijuan;QIAN Kaiqi;FU Yu;WU Chenjun;LIU Baoshan(School of Information,North China University of Technology,Beijing 100144,China)
出处
《现代信息科技》
2023年第8期75-78,共4页
Modern Information Technology
基金
国家级大学生创新训练项目资助(108051360022XN224)。
作者简介
李伟娟(2001-),女,汉族,山东菏泽人,本科在读,研究方向:通信工程;千凯琦(2002-),男,汉族,河南焦作人,本科在读,研究方向:通信工程。
