{"title":"基于卷积神经网络的道路检测在移动机器人定位中的应用","authors":"J. Krejsa, S. Vechet","doi":"10.21495/71-0-203","DOIUrl":null,"url":null,"abstract":"Mobile robot on-road navigation requires fusion of both global and local sensory information with an emphasis on the road detection processing. The paper deals with the road detection based on convolution neural networks (CNN) using commonly available tools such as TensorFlow and Keras. The road is defined by its linear boundaries. Network output is formed by the road definition together with classification parameters and serves as a local sensor in Kalman filter based localization. CNN based road detection is currently capable to successfully detect about 90% of images.","PeriodicalId":197313,"journal":{"name":"Engineering Mechanics 2019","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"UTILIZATION OF CONVOLUTION NEURAL NETWORK BASED ROAD DETECTION IN MOBILE ROBOT LOCALIZATION\",\"authors\":\"J. Krejsa, S. Vechet\",\"doi\":\"10.21495/71-0-203\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mobile robot on-road navigation requires fusion of both global and local sensory information with an emphasis on the road detection processing. The paper deals with the road detection based on convolution neural networks (CNN) using commonly available tools such as TensorFlow and Keras. The road is defined by its linear boundaries. Network output is formed by the road definition together with classification parameters and serves as a local sensor in Kalman filter based localization. CNN based road detection is currently capable to successfully detect about 90% of images.\",\"PeriodicalId\":197313,\"journal\":{\"name\":\"Engineering Mechanics 2019\",\"volume\":\"73 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Mechanics 2019\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21495/71-0-203\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Mechanics 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21495/71-0-203","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
UTILIZATION OF CONVOLUTION NEURAL NETWORK BASED ROAD DETECTION IN MOBILE ROBOT LOCALIZATION
Mobile robot on-road navigation requires fusion of both global and local sensory information with an emphasis on the road detection processing. The paper deals with the road detection based on convolution neural networks (CNN) using commonly available tools such as TensorFlow and Keras. The road is defined by its linear boundaries. Network output is formed by the road definition together with classification parameters and serves as a local sensor in Kalman filter based localization. CNN based road detection is currently capable to successfully detect about 90% of images.
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