Exploring Edge Computing for Sustainable CV-Based Worker Detection in Construction Site Monitoring: Performance and Feasibility Analysis

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.