Research on the Automated Analysis and Processing System for Campus Safety Events Based on Deep Learning

Timely detection and handling of campus safety incidents are important aspects of educational management, and traditional manual monitoring models can no longer meet the needs of real-time supervision. This study designs and implements an automated campus safety event processing system based on deep learning technology. By improving the YOLOv5 model structure and introducing the CBAM attention mechanism, combined with spatiotemporal correlation analysis algorithms, the system achieves intelligent detection and processing of typical safety events such as fights, intrusions, fire alarms, and gatherings. System tests show that this approach has significant advantages in detection accuracy, response time, and concurrent processing capabilities. The research provides a new technological solution for campus safety management with important practical application value.