Design and implementation of an intelligent fire monitoring system
Considering the deficiencies in dynamic fire situation visualization,accuracy,and the issues of false and missed alarms in existing fire monitoring systems,an efficient intelligent fire monitoring system has been designed and implemented.This paper adopts the improved YOLOv5s6 algorithm model,which optimizes the algorithm by changing the network structure and combines it with dynamic fire situation visualization to achieve higher accuracy and faster monitoring speed,effectively reducing false and missed alarms.The system,based on the improvement of YOLOv5s,mainly implements core functions such as static image,dynamic video,and real-time video monitoring,user management,database log management,and real-time early warning.The system has significantly improved in monitoring accuracy and response speed,and the addition of dynamic fire situation visualization makes fire monitoring more intuitive and efficient.The system has good universality,with a user-friendly interface built by PyQT5,MySQL for persistent data storage and efficient management,and supports cross-platform installation and deployment through Pyinstaller.
fire monitoringYOLOv5 algorithmobject detection algorithmcomputer visionintelligent alarm system
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