基于边缘计算的智能化建筑安全监控系统
Intelligent Building Safety Monitoring System Based on Edge Computing
郭宇丰 1董亚杰 1李艳 1李浩 1王娜 2王联旭2
作者信息
- 1. 昆明冶金高等专科学校 建筑工程学院,云南 昆明 650033
- 2. 昆明冶金高等专科学校学生处,云南 昆明 650033
- 折叠
摘要
探讨基于边缘计算的智能化建筑安全监控系统,以解决智能化建筑安全监控的挑战.首先分析智能化建筑安全监控的需求和挑战;其次设计基于边缘计算的监控系统架构,采用Yolo和SlowFast模型实现了人脸识别、目标检测、姿态识别和行为检测等功能;最后通过在UCF-101 数据集上进行实验测试,评估系统的性能.结果显示,基于SlowFast模型的姿态识别和行为检测在准确率、召回率和F1 值等指标上表现良好,系统有效、可靠,提出的基于边缘计算的智能化建筑安全监控系统能够有效解决智能化建筑安全监控的问题,为建筑安全管理提供了可靠的技术支持.未来,可进一步优化算法和系统架构,以提升系统的性能和可靠性,满足不断增长的建筑安全管理需求.
Abstract
This study aims to explore an intelligent building safety monitoring system based on edge com-puting to address the challenges of intelligent building safety monitoring.Firstly,the needs and challen-ges of intelligent building safety monitoring were analyzed,establishing the purpose of this study.Second-ly,a monitoring system architecture based on edge computing was designed,utilizing Yolo and SlowFast models to implement functions such as face recognition,target detection,pose recognition,and behavior detection.Subsequently,the system's performance was evaluated through experimental testing on the UCF-101 dataset.The results show that pose recognition and behavior detection based on the SlowFast model perform well in terms of accuracy,recall,and F1 score,demonstrating the system's effectiveness and re-liability.The conclusion indicates that the intelligent building safety monitoring system based on edge computing proposed in this study effectively addresses the problem of intelligent building safety monito-ring,providing reliable technical support for building safety management.In the future,algorithms and system architecture can be further optimized to enhance system performance and reliability,meeting the growing demand for building safety management.
关键词
边缘计算/视频监控/Yolo模型/SlowFast模型Key words
edge computing/video surveillance/Yolo model/SlowFast model引用本文复制引用
出版年
2024
NETL
NSTL
万方数据