Research And Construction Of Bayonet Video Monitoring System Based On Deep Learning

In recent years,the frequent occurrence of forest fires in China has caused great harm to the environment and economy.With the rapid development of forestry in China and the increasing forest coverage rate year by year,forest fire prevention is facing huge challenges.It is an important measure for forest fire prevention to do timely monitoring and prevention.Therefore,the research and construction of a forest area bayonet monitoring system is very important for the prevention of forest fires.This system is based on B/S architecture,applying target detection technology based on deep leaning and server-side development technology based on Spring Boot framework.Following the MVC design idea,the system architecture is divided into application layer,control layer,business logic layer and The persistence layer implements the server management system part by integrating tools such as Spring Security permission control framework,ECharts chart library,and MySQL database,and builds the human vehicle detection module part by using OpenCV.This system is based on the monitoring video of the bayonet in the forest area,and transmits the monitoring video of the bayonet to the server to monitor the entrance of the forest area in real time and identify it through the human vehicle detection module.Forest staff can perform video surveillance,surveillance point management,surveillance video management,surveillance screenshot management,data analysis and system management through the background management system.The forest area bayonet monitoring system finally realized the functions including target detection of personnel and vehicles,storage and display of monitoring data,message push and alarm,data statistics and visualization.The system standardizes the processing procedures of transmission,detection,storage,display and analysis of the bayonet surveillance video,and provides timely feedback and alarms to improve the timeliness of information transfer and processing of forest staff,and early detection of hidden fire hazards from the source To achieve the purpose of reducing the loss of forest resources.