{"title":"用于室内辅助导航的室内标识检测系统","authors":"Mouna Afif, R. Ayachi, Yahia Said, M. Atri","doi":"10.1109/SSD52085.2021.9429495","DOIUrl":null,"url":null,"abstract":"Indoor signage plays an important role in finding specific destinations and way-finding especially for blind and visually impaired people (VIP). In this paper, we developed a new indoor signage classifier using deep convolutional neural Network (DCNN). Computer vision-based systems using cameras-based present a potential intermediate to assist blind and VIP persons on accessing unfamiliar buildings. Experiments were performed on a new dataset taken in an indoor building in France. The proposed dataset present 800 natural images divided into 4 indoor signs. Results achieved show that our proposed approach presents very encouraging results coming to 99.8% as recognition precision rate.","PeriodicalId":6799,"journal":{"name":"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)","volume":"11 1","pages":"1383-1387"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Indoor sign Detection System for Indoor Assistance Navigation\",\"authors\":\"Mouna Afif, R. Ayachi, Yahia Said, M. Atri\",\"doi\":\"10.1109/SSD52085.2021.9429495\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Indoor signage plays an important role in finding specific destinations and way-finding especially for blind and visually impaired people (VIP). In this paper, we developed a new indoor signage classifier using deep convolutional neural Network (DCNN). Computer vision-based systems using cameras-based present a potential intermediate to assist blind and VIP persons on accessing unfamiliar buildings. Experiments were performed on a new dataset taken in an indoor building in France. The proposed dataset present 800 natural images divided into 4 indoor signs. Results achieved show that our proposed approach presents very encouraging results coming to 99.8% as recognition precision rate.\",\"PeriodicalId\":6799,\"journal\":{\"name\":\"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)\",\"volume\":\"11 1\",\"pages\":\"1383-1387\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSD52085.2021.9429495\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSD52085.2021.9429495","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Indoor sign Detection System for Indoor Assistance Navigation
Indoor signage plays an important role in finding specific destinations and way-finding especially for blind and visually impaired people (VIP). In this paper, we developed a new indoor signage classifier using deep convolutional neural Network (DCNN). Computer vision-based systems using cameras-based present a potential intermediate to assist blind and VIP persons on accessing unfamiliar buildings. Experiments were performed on a new dataset taken in an indoor building in France. The proposed dataset present 800 natural images divided into 4 indoor signs. Results achieved show that our proposed approach presents very encouraging results coming to 99.8% as recognition precision rate.
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