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
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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。
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.
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