{"title":"Detecting Health & Safety Hazards through AI and Edge Computing on Mobile Devices","authors":"C. Panagiotou, Lidia Pocero Fraile, C. Koulamas","doi":"10.1109/MECO58584.2023.10155077","DOIUrl":null,"url":null,"abstract":"Safety hazards in working environments introduce significant risks for the health of the people that are active in these environments. The prevention of such incidents becomes a high priority for safety officers. The prevention mechanisms regard compliance with safety standards and auditing of the conditions of the workplaces. This paper, presents an AI based solution that aims to identify safety risks and is able to operate in the edge focusing mobile devices. The presented approach trains an SSD Mobilenet v2 based model with a data set focused on the detection of conditions that might introduce risks for the people and the infrastructure. The trained model has been integrated in a mobile application to utilize the high quality video streams capture by modern smartphones.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MECO58584.2023.10155077","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
Safety hazards in working environments introduce significant risks for the health of the people that are active in these environments. The prevention of such incidents becomes a high priority for safety officers. The prevention mechanisms regard compliance with safety standards and auditing of the conditions of the workplaces. This paper, presents an AI based solution that aims to identify safety risks and is able to operate in the edge focusing mobile devices. The presented approach trains an SSD Mobilenet v2 based model with a data set focused on the detection of conditions that might introduce risks for the people and the infrastructure. The trained model has been integrated in a mobile application to utilize the high quality video streams capture by modern smartphones.
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