{"title":"基于深度学习技术的交通标志识别","authors":"Yihan Lai","doi":"10.1145/3546157.3546167","DOIUrl":null,"url":null,"abstract":"Traffic sign recognition plays a significant role in intelligent transportation system. Therefore, in this paper, I propose a traffic sign recognition algorithm based on Convolutional Neural Network (CNN). The dataset collected to train and test in experiments is the “German Traffic Sign Recognition Benchmark” (GTSRB). In addition, the CNN model is evaluated by comparing with a Deep Neural Network (DNN) model based on the accuracy rate and loss rate. Finally, the result shows the proposed CNN model yields high accuracy rate on both training and test images.","PeriodicalId":422215,"journal":{"name":"Proceedings of the 6th International Conference on Information System and Data Mining","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Traffic Sign Recognition Based on Deep Learning Technique\",\"authors\":\"Yihan Lai\",\"doi\":\"10.1145/3546157.3546167\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traffic sign recognition plays a significant role in intelligent transportation system. Therefore, in this paper, I propose a traffic sign recognition algorithm based on Convolutional Neural Network (CNN). The dataset collected to train and test in experiments is the “German Traffic Sign Recognition Benchmark” (GTSRB). In addition, the CNN model is evaluated by comparing with a Deep Neural Network (DNN) model based on the accuracy rate and loss rate. Finally, the result shows the proposed CNN model yields high accuracy rate on both training and test images.\",\"PeriodicalId\":422215,\"journal\":{\"name\":\"Proceedings of the 6th International Conference on Information System and Data Mining\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 6th International Conference on Information System and Data Mining\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3546157.3546167\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th International Conference on Information System and Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3546157.3546167","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Traffic Sign Recognition Based on Deep Learning Technique
Traffic sign recognition plays a significant role in intelligent transportation system. Therefore, in this paper, I propose a traffic sign recognition algorithm based on Convolutional Neural Network (CNN). The dataset collected to train and test in experiments is the “German Traffic Sign Recognition Benchmark” (GTSRB). In addition, the CNN model is evaluated by comparing with a Deep Neural Network (DNN) model based on the accuracy rate and loss rate. Finally, the result shows the proposed CNN model yields high accuracy rate on both training and test images.