{"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}
引用次数: 0
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.
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