建筑工地自动巡检系统(ici -智能摄像头巡检)

Maitha Rashed Al Zaabi, Houraa Alhendi, Hessa Alkhoori, Maryam Alkhoori, Shama Alkhemriri, Yasmeen Abu-Kheil
{"title":"建筑工地自动巡检系统(ici -智能摄像头巡检)","authors":"Maitha Rashed Al Zaabi, Houraa Alhendi, Hessa Alkhoori, Maryam Alkhoori, Shama Alkhemriri, Yasmeen Abu-Kheil","doi":"10.1109/ASET53988.2022.9734932","DOIUrl":null,"url":null,"abstract":"Current practices of predicting the performance of a construction team require inspections that are still mainly manual, time-consuming, and can contain errors. In this paper, we proposed an automated site inspection and violation detection system that can be used in construction sites. Our proposed system uses deep learning SqueezeNet pre-trained network to detect two main violations: i) health & safety and ii) electrical hazards. The accuracy of our proposed system is 91.67% in detecting health & safety violations related to none-wearing personal protective equipment (PPE) while the accuracy of our proposed system reached 92.86% in detecting electrical hazards.","PeriodicalId":6832,"journal":{"name":"2022 Advances in Science and Engineering Technology International Conferences (ASET)","volume":"31 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automatic Site Inspection System in Construction Sites (ICI–Intelligent Camera Inspection)\",\"authors\":\"Maitha Rashed Al Zaabi, Houraa Alhendi, Hessa Alkhoori, Maryam Alkhoori, Shama Alkhemriri, Yasmeen Abu-Kheil\",\"doi\":\"10.1109/ASET53988.2022.9734932\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Current practices of predicting the performance of a construction team require inspections that are still mainly manual, time-consuming, and can contain errors. In this paper, we proposed an automated site inspection and violation detection system that can be used in construction sites. Our proposed system uses deep learning SqueezeNet pre-trained network to detect two main violations: i) health & safety and ii) electrical hazards. The accuracy of our proposed system is 91.67% in detecting health & safety violations related to none-wearing personal protective equipment (PPE) while the accuracy of our proposed system reached 92.86% in detecting electrical hazards.\",\"PeriodicalId\":6832,\"journal\":{\"name\":\"2022 Advances in Science and Engineering Technology International Conferences (ASET)\",\"volume\":\"31 1\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Advances in Science and Engineering Technology International Conferences (ASET)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASET53988.2022.9734932\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Advances in Science and Engineering Technology International Conferences (ASET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASET53988.2022.9734932","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

预测构建团队性能的当前实践需要的检查仍然主要是手工的、耗时的,并且可能包含错误。在本文中,我们提出了一种可用于建筑工地的自动化现场检查和违规检测系统。我们提出的系统使用深度学习SqueezeNet预训练网络来检测两种主要违规行为:i)健康与安全;ii)电气危险。我们提出的系统在检测与不佩戴个人防护设备(PPE)相关的健康和安全违规方面的准确率为91.67%,而我们提出的系统在检测电气危害方面的准确率为92.86%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Automatic Site Inspection System in Construction Sites (ICI–Intelligent Camera Inspection)
Current practices of predicting the performance of a construction team require inspections that are still mainly manual, time-consuming, and can contain errors. In this paper, we proposed an automated site inspection and violation detection system that can be used in construction sites. Our proposed system uses deep learning SqueezeNet pre-trained network to detect two main violations: i) health & safety and ii) electrical hazards. The accuracy of our proposed system is 91.67% in detecting health & safety violations related to none-wearing personal protective equipment (PPE) while the accuracy of our proposed system reached 92.86% in detecting electrical hazards.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Numerical computing in engineering mathematics A Model for Solar Home System Assessment in Public Housing Projects in the United Arab Emirates A hybrid photovoltaic/solar chimney seawater desalination plant Comparative Study Investigating Compressed Natural Gas, Diesel, and Gasoline as Fuel for the Transportation Sector: A Case of UAE Impact of Urbanisation on Surface Temperature using Satellite and Ground Observations
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1