传统的校园霸凌干预措施主要依赖于教师和学生的主动报告以及学校管理层的定期巡查和查看监控,但这些方法往往存在滞后性和过多的人力消耗,难以及时发现和处理霸凌行为。基于深度学习的校园霸凌监控系统,旨在为校园安全提供有力保障,为学生的心理健康提供保障。本研究以YOLOv8目标检测算法和3D ResNet行为分析为核心技术,采用舵机动态跟踪技术,结合目标检测、行为分析、动态跟踪等模块,构建了一个全方位、智能化的校园霸凌监控系统。系统通过对校园内监控实时视频流的高效处理和深度分析,能够快速准确地识别潜在的霸凌行为,并及时发出预警通知,以便管理人员迅速采取干预措施。此外,该系统还配备了“微光心愈屋”小程序,为受害者提供情感支持和心理慰藉,帮助其缓解心理压力,结合AI智能聊天功能,帮助学生和教师更好地了解校园霸凌安全问题,营造一个更加安全、和谐的校园环境。

Traditional anti-bullying interventions in schools rely primarily on proactive reports from teachers and students, together with periodic inspections and surveillance by administrators. These approaches are untimely reactive and labor-intensive, which makes timely detection and response to bullying incidents difficult. A deep-learning-based campus-bullying monitoring system that provides robust protection for campus security and safeguards the mental health of students. The system integrates YOLOv8 for object detection and 3D ResNet for action recognition as its core algorithms, while leveraging servo-driven dynamic tracking as its enabling hardware technology. By combining modules for object detection, action analysis, and dynamic tracking, we construct a comprehensive and intelligent monitoring framework. By processing and analyzing live surveillance streams in real time, the system can rapidly and accurately identify potential bullying behaviors and promptly issue early-warning notifications so that administrators can intervene without delay. In addition, the system includes the “Glimmer Haven” mini-program, which offers emotional support and psychological solace to victims, alleviating their mental distress. An AI-powered chatbot further assists students and faculty in understanding and addressing campus-bullying issues, thereby fostering a safer and more harmonious school environment.