Xue Xiao, Chen Chen, Martin Skitmore, Heng Li, Yue Deng
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引用次数: 0
Abstract
This research explores edge computing for construction site monitoring using computer vision (CV)-based worker detection methods. The feasibility of using edge computing is validated by testing worker detection models (yolov5 and yolov8) on local computers and three edge computing devices (Jetson Nano, Raspberry Pi 4B, and Jetson Xavier NX). The results show comparable mAP values for all devices, with the local computer processing frames six times faster than the Jetson Xavier NX. This study contributes by proposing an edge computing solution to address data security, installation complexity, and time delay issues in CV-based construction site monitoring. This approach also enhances data sustainability by mitigating potential risks associated with data loss, privacy breaches, and network connectivity issues. Additionally, it illustrates the practicality of employing edge computing devices for automated visual monitoring and provides valuable information for construction managers to select the appropriate device.
期刊介绍：
BUILDINGS content is primarily staff-written and submitted information is evaluated by the editors for its value to the audience. Such information may be used in articles with appropriate attribution to the source. The editorial staff considers information on the following topics: -Issues directed at building owners and facility managers in North America -Issues relevant to existing buildings, including retrofits, maintenance and modernization -Solution-based content, such as tips and tricks -New construction but only with an eye to issues involving maintenance and operation We generally do not review the following topics because these are not relevant to our readers: -Information on the residential market with the exception of multifamily buildings -International news unrelated to the North American market -Real estate market updates or construction updates
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