{"title":"基于神经网络的自动驾驶交通标志识别","authors":"George-Zamfir Tiron, M. Poboroniuc","doi":"10.1109/SIELMEN.2019.8905903","DOIUrl":null,"url":null,"abstract":"Autonomous and self-driving cars represent a certitude for the near future and it is expected to be safer, time saving and to bring in environmental benefits. The road infrastrucuture still embeds traffic signs that have to be recognized with high accuracy by the sensorial system mounted on an autonomous car. This paper deals with a new Traffic Sign Recognitions system based on a pretrained neural network for general objects and retrained for a large number of traffic signs collected in real environment under different metrorological conditions. The proposed Traffic Sign Recognition system is able to classify signs in new scenarios with more than 96% accuracy.","PeriodicalId":129030,"journal":{"name":"2019 International Conference on Electromechanical and Energy Systems (SIELMEN)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Neural Network Based Traffic Sign Recognition for Autonomous Driving\",\"authors\":\"George-Zamfir Tiron, M. Poboroniuc\",\"doi\":\"10.1109/SIELMEN.2019.8905903\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Autonomous and self-driving cars represent a certitude for the near future and it is expected to be safer, time saving and to bring in environmental benefits. The road infrastrucuture still embeds traffic signs that have to be recognized with high accuracy by the sensorial system mounted on an autonomous car. This paper deals with a new Traffic Sign Recognitions system based on a pretrained neural network for general objects and retrained for a large number of traffic signs collected in real environment under different metrorological conditions. The proposed Traffic Sign Recognition system is able to classify signs in new scenarios with more than 96% accuracy.\",\"PeriodicalId\":129030,\"journal\":{\"name\":\"2019 International Conference on Electromechanical and Energy Systems (SIELMEN)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Electromechanical and Energy Systems (SIELMEN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIELMEN.2019.8905903\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Electromechanical and Energy Systems (SIELMEN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIELMEN.2019.8905903","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neural Network Based Traffic Sign Recognition for Autonomous Driving
Autonomous and self-driving cars represent a certitude for the near future and it is expected to be safer, time saving and to bring in environmental benefits. The road infrastrucuture still embeds traffic signs that have to be recognized with high accuracy by the sensorial system mounted on an autonomous car. This paper deals with a new Traffic Sign Recognitions system based on a pretrained neural network for general objects and retrained for a large number of traffic signs collected in real environment under different metrorological conditions. The proposed Traffic Sign Recognition system is able to classify signs in new scenarios with more than 96% accuracy.