基于改进YOLOv7算法的学生课堂行为识别研究  

Research on Student Classroom Behavior Recognition Based on the Improved YOLOv7 Algorithm

作　　者：张小妮 杨萌萌 张军锋 苏利萍 ZHANG Xiaoni;YANG Mengmeng;ZHANG Junfeng;SU Liping(Henan Vocational College of Water Conservancy and Environment,Zhengzhou 450008,China)

机构地区：[1]河南水利与环境职业学院,河南郑州450008

出　　处：《现代信息科技》2025年第4期69-73,共5页Modern Information Technology

基　　金：河南省科技攻关(242102211054)。

摘　　要：学生课堂行为识别能够有效提升课堂教学效果,是智慧教育不可或缺的一环。鉴于缺乏相关研究数据,文章首先构建了学生课堂行为数据集。在特殊的课堂环境中,学生数量众多且常相互遮挡,后排学生目标体积较小,所以在复杂多变的环境下,难以将学生行为与周围背景区分开来。因此,文章提出一种基于改进YOLOv7目标检测算法的学生行为识别方法(YL7CA),将CA注意力机制嵌入到YOLOv7中,以便更准确地检测学生行为。该方法在自建数据集上获得了92.6%的检测精度,能有效检测出抬头、低头、转头、玩手机、读写、睡觉这六类常见的学生课堂行为。Student classroom behavior recognition can effectively improve the effect of classroom teaching,which is an indispensable part of smart education.In view of the lack of relevant research data,this paper first constructs a dataset of student classroom behavior.In the special classroom environment,there are a large number of students and they often block each other,and the volume of the rear student target is small.Therefore,in the complex and changeable environment,it is difficult to distinguish the student behavior from the surrounding background.Therefore,this paper proposes a student behavior recognition method based on the improved YOLOv7 object detection algorithm (YL7CA),which embeds CA Attention Mechanism into YOLOv7 to detect student behavior more accurately.This method obtains a detection accuracy of 92.6% on the self-built dataset,and can effectively detect six common types of student classroom behaviors,including looking up,looking down,turning around,playing on mobile phones,reading and writing,and sleeping.

关 键 词：YOLOv7 行为识别 注意力机制 目标检测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391.4[自动化与计算机技术—控制科学与工程]