Dynamic Optimization Research on Regional Security Planning of College Dormitory under Neural Network Perspective

Abstract

A dormitory is a place where students live and live for a long time, and the safety of the dormitory can directly affect the quality of students' studies and lives. In light of this, the study constructs a dynamic security planning system for college dormitory areas, enhancing their security and intelligence through the application of intelligent technology and multi-directional management coordination. Taking the access control system and fire monitoring system in the dormitory security prevention system as an example, based on the neural network perspective, we design the face recognition method of HOG-YOLO and the fire detection algorithm of improved YOLOv5 to realize, train, and test the two models, and evaluate the face recognition performance and fire target detection effect of the models. The results show that the face recognition method established in this paper is highly accurate and rapid, with a recognition accuracy of 95.7%, and it can complete recognition and detection under different facial angles. The fire detection algorithm's FPS has been improved by 14.75%. The overall accuracy has improved by more than 1%, which has a good effect on fire monitoring. Planning the security system for the dormitory areas in higher education is a significant area of research for ensuring campus security, and access control and fire monitoring systems have significant application potential.