现代信息科技2025，Vol.9Issue(5)：72-77,6.DOI:10.19850/j.cnki.2096-4706.2025.05.013
基于YOLOv8的校园候车点人群计数系统设计与实现
Design and Implementation of Campus Waiting Point Crowd Counting System Based on YOLOv8
摘要
Abstract
Currently,the construction of smart campus in China is booming,but there are still deficiencies in the campus waiting point crowd counting.Due to the overlapping recognition targets and the complex background environment,it is difficult to balance the recognition speed and accuracy.Aiming at this problem,this paper proposes a method based on CNN and YOLOv8 models.Through deep exploration and analysis of the innovative points of YOLOv8 model,a campus waiting point crowd counting system based on YOLOv8 is designed.Finally,the average accuracy of the model on the self-designed dataset reaches 87.4%.In addition,combined with visual design,it is integrated into the system to realize a relatively accurate counting of waiting crowd,which provides help to solve the problem of crowd counting at campus waiting points.关键词
人群计数/CNN/YOLOv8/深度学习/目标检测Key words
crowd counting/CNN/YOLOv8/Deep Learning/Object Detection分类
信息技术与安全科学引用本文复制引用
陈俊..基于YOLOv8的校园候车点人群计数系统设计与实现[J].现代信息科技,2025,9(5):72-77,6.基金项目
贵州大学大学生创新创业训练计划项目(gzusc2023056) （gzusc2023056）
