现代信息科技2024，Vol.8Issue(11)：106-110,5.DOI:10.19850/j.cnki.2096-4706.2024.11.021
实验教学课堂学生表情检测系统设计与实现
Design and Implementation of a Student Expression Detection System in Experimental Teaching Classroom
摘要
Abstract
In response to the difficulty in monitoring the learning effectiveness of students in experimental teaching classrooms,the SE Attention Mechanism and improved spatial pyramid pooling are introduced into YOLOv5.A student expression detection system based on low-quality laboratory videos is designed,achieving high-precision recognition of facial expressions of students in experimental classrooms.The experimental results show that after adding the SE Attention Mechanism module,the recognition accuracy of the model reaches 89%.After adding improved pyramid pooling,the model recognition accuracy reaches 94%.The system combines Deep Learning technology with laboratory classroom teaching quality evaluation practice,innovates the experimental teaching classroom quality evaluation mode,and can provide reference basis for teachers to adjust the experimental classroom teaching mode.关键词
低画质视频/表情检测/YOLOv5/注意力机制/空间金字塔池化Key words
low-quality video/expression detection/YOLOv5/Attention Mechanism/spatial pyramid pooling分类
计算机与自动化引用本文复制引用
吴斌..实验教学课堂学生表情检测系统设计与实现[J].现代信息科技,2024,8(11):106-110,5.基金项目
浙江省教育厅科研资助项目(Y202250093) （Y202250093）
浙江农林大学科研发展基金项目(2023LFR147) （2023LFR147）
