基于YOLOv5的校园公共空间人流密度识别与监测  

Study on the Identification and Monitoring of Crowd Density in Campus Public Spaces based on YOLOv5

作　　者：于小莉 李帅 Yu Xiaoli;Li Shuai(School of Information Engineering,Changji College,Changji 831100)

机构地区：[1]昌吉学院信息工程学院,新疆昌吉831100

出　　处：《中阿科技论坛(中英文)》2025年第2期87-91,共5页China-Arab States Science and Technology Forum

基　　金：昌吉学院2024年度校级科研项目“新疆地方高校校园公共空间人流密度识别与监测系统”(KY2024021)。

摘　　要：随着人工智能技术的不断发展,目标检测和目标追踪等技术在许多领域得到了广泛应用。其中,人流密度监控是校园安全管理等领域中的一个重要应用。文章设计并实现了一套基于YOLOv5算法的校园公共空间人流密度识别与监测系统,以提升校园安全管理的效率和准确性。系统采用Pyside6图形界面框架,并利用Qt Designer工具精心设计用户友好的可视化操作界面。通过对特定场景数据集的深度优化和训练,系统灵活调整监测路径,以适应复杂环境和遮挡情况,精准测算校园公共空间的人流密度。试验结果表明,该监测系统有助于校园管理人员及时发现异常情况,最大限度地防范相关风险,提高校园安全管理水平。With the booming of AI,target detection and tracking technologies have been extensively applied in various fields.Among them,crowd density monitoring is an important part of campus safety.In view of this,the article designs a crowd density recognition and monitoring system based on YOLOv5 algorithm to enhance the efficiency and accuracy of safety management.The system adopts Pyside6 graphical interface framework and utilizes the Qt Designer tool to elaborate a user-friendly visual operation interface.By optimizing and training scene-specific datasets,the monitoring paths are flexibly adjusted to adapt to complex environments and occlusion.The experiment shows that the monitoring system helps management personnel to detect abnormal situations in a timely manner,minimize related risks,and improve campus safety.

关 键 词：YOLOv5 Pyside6 人流密度 目标监测 校园安全 

分 类 号：TP75[自动化与计算机技术—检测技术与自动化装置]